Overview

Dataset statistics

Number of variables	74
Number of observations	5142
Missing cells	146238
Missing cells (%)	38.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	27.2 MiB
Average record size in memory	5.4 KiB

Variable types

Numeric	9
Categorical	12
Text	48
Unsupported	2
DateTime	1
Boolean	2

Alerts

`DOI Link` has constant value "0.0"	Constant
`Highly Cited Status` has constant value "True"	Constant
`Hot Paper Status` has constant value "False"	Constant
`Date of Export` has constant value "2025-12-30"	Constant
`Web of Science Record` has constant value "0"	Constant
`180 Day Usage Count` is highly overall correlated with `Book DOI` and 6 other fields	High correlation
`Book DOI` is highly overall correlated with `180 Day Usage Count` and 10 other fields	High correlation
`Book Group Authors` is highly overall correlated with `180 Day Usage Count` and 7 other fields	High correlation
`Cited Reference Count` is highly overall correlated with `180 Day Usage Count` and 2 other fields	High correlation
`Document Type` is highly overall correlated with `Book Group Authors` and 1 other fields	High correlation
`Language` is highly overall correlated with `Book DOI` and 1 other fields	High correlation
`Number of Pages` is highly overall correlated with `180 Day Usage Count` and 4 other fields	High correlation
`Open Access Designations` is highly overall correlated with `Book DOI` and 2 other fields	High correlation
`Publication Type` is highly overall correlated with `Book DOI` and 5 other fields	High correlation
`Publication Year` is highly overall correlated with `180 Day Usage Count` and 2 other fields	High correlation
`Pubmed Id` is highly overall correlated with `Language` and 5 other fields	High correlation
`Since 2013 Usage Count` is highly overall correlated with `180 Day Usage Count` and 5 other fields	High correlation
`Special Issue` is highly overall correlated with `Open Access Designations` and 5 other fields	High correlation
`Supplement` is highly overall correlated with `180 Day Usage Count` and 6 other fields	High correlation
`Times Cited, All Databases` is highly overall correlated with `Book DOI` and 6 other fields	High correlation
`Times Cited, WoS Core` is highly overall correlated with `Book DOI` and 6 other fields	High correlation
`Web of Science Index` is highly overall correlated with `Book DOI` and 2 other fields	High correlation
`Language` is highly imbalanced (92.3%)	Imbalance
`Document Type` is highly imbalanced (60.8%)	Imbalance
`Supplement` is highly imbalanced (52.4%)	Imbalance
`Special Issue` is highly imbalanced (92.1%)	Imbalance
`Book Authors` has 5100 (99.2%) missing values	Missing
`Book Editors` has 3818 (74.3%) missing values	Missing
`Book Group Authors` has 4017 (78.1%) missing values	Missing
`Book Author Full Names` has 5100 (99.2%) missing values	Missing
`Group Authors` has 5140 (> 99.9%) missing values	Missing
`Book Series Title` has 3911 (76.1%) missing values	Missing
`Book Series Subtitle` has 5142 (100.0%) missing values	Missing
`Conference Title` has 2751 (53.5%) missing values	Missing
`Conference Date` has 2751 (53.5%) missing values	Missing
`Conference Location` has 2751 (53.5%) missing values	Missing
`Conference Sponsor` has 3453 (67.2%) missing values	Missing
`Conference Host` has 4619 (89.8%) missing values	Missing
`Author Keywords` has 457 (8.9%) missing values	Missing
`Keywords Plus` has 2366 (46.0%) missing values	Missing
`Abstract` has 164 (3.2%) missing values	Missing
`Affiliations` has 290 (5.6%) missing values	Missing
`Reprint Addresses` has 92 (1.8%) missing values	Missing
`Email Addresses` has 454 (8.8%) missing values	Missing
`Researcher Ids` has 1794 (34.9%) missing values	Missing
`ORCIDs` has 1775 (34.5%) missing values	Missing
`Funding Orgs` has 2820 (54.8%) missing values	Missing
`Funding Name Preferred` has 2841 (55.3%) missing values	Missing
`Funding Text` has 2826 (55.0%) missing values	Missing
`Cited References` has 5142 (100.0%) missing values	Missing
`ISSN` has 1667 (32.4%) missing values	Missing
`eISSN` has 2585 (50.3%) missing values	Missing
`ISBN` has 2737 (53.2%) missing values	Missing
`Journal Abbreviation` has 1242 (24.2%) missing values	Missing
`Journal ISO Abbreviation` has 2443 (47.5%) missing values	Missing
`Publication Date` has 2766 (53.8%) missing values	Missing
`Volume` has 2192 (42.6%) missing values	Missing
`Issue` has 3041 (59.1%) missing values	Missing
`Part Number` has 5133 (99.8%) missing values	Missing
`Supplement` has 5125 (99.7%) missing values	Missing
`Special Issue` has 4937 (96.0%) missing values	Missing
`Meeting Abstract` has 5138 (99.9%) missing values	Missing
`Start Page` has 1440 (28.0%) missing values	Missing
`End Page` has 1440 (28.0%) missing values	Missing
`Article Number` has 4175 (81.2%) missing values	Missing
`DOI` has 950 (18.5%) missing values	Missing
`DOI Link` has 950 (18.5%) missing values	Missing
`Book DOI` has 5069 (98.6%) missing values	Missing
`Early Access Date` has 3926 (76.4%) missing values	Missing
`Pubmed Id` has 4979 (96.8%) missing values	Missing
`Open Access Designations` has 3457 (67.2%) missing values	Missing
`Highly Cited Status` has 5128 (99.7%) missing values	Missing
`Hot Paper Status` has 5128 (99.7%) missing values	Missing
`doi_norm` has 950 (18.5%) missing values	Missing
`Times Cited, WoS Core` is highly skewed (γ1 = 39.49011734)	Skewed
`Times Cited, All Databases` is highly skewed (γ1 = 40.13761972)	Skewed
`Unnamed: 0` is uniformly distributed	Uniform
`Unnamed: 0` has unique values	Unique
`UT (Unique WOS ID)` has unique values	Unique
`Book Series Subtitle` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Cited References` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Cited Reference Count` has 114 (2.2%) zeros	Zeros
`Times Cited, WoS Core` has 1472 (28.6%) zeros	Zeros
`Times Cited, All Databases` has 1307 (25.4%) zeros	Zeros
`180 Day Usage Count` has 2249 (43.7%) zeros	Zeros
`Since 2013 Usage Count` has 430 (8.4%) zeros	Zeros

Reproduction

Analysis started	2026-01-14 11:00:37.242254
Analysis finished	2026-01-14 11:01:18.731627
Duration	41.49 seconds
Software version	ydata-profiling vv4.18.1
Download configuration	config.json

Unnamed: 0
Real number (ℝ)

Uniform Unique

Distinct	5142
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2570.5

Minimum	0
Maximum	5141
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	257.05
Q1	1285.25
median	2570.5
Q3	3855.75
95-th percentile	4883.95
Maximum	5141
Range	5141
Interquartile range (IQR)	2570.5

Descriptive statistics

Standard deviation	1484.5119
Coefficient of variation (CV)	0.57751872
Kurtosis	-1.2
Mean	2570.5
Median Absolute Deviation (MAD)	1285.5
Skewness	0
Sum	13217511
Variance	2203775.5
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5141	1	< 0.1%
0	1	< 0.1%
1	1	< 0.1%
2	1	< 0.1%
3	1	< 0.1%
4	1	< 0.1%
5125	1	< 0.1%
5124	1	< 0.1%
5123	1	< 0.1%
5122	1	< 0.1%
Other values (5132)	5132	99.8%

Value	Count	Frequency (%)
0	1	< 0.1%
1	1	< 0.1%
2	1	< 0.1%
3	1	< 0.1%
4	1	< 0.1%
5	1	< 0.1%
6	1	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
5141	1	< 0.1%
5140	1	< 0.1%
5139	1	< 0.1%
5138	1	< 0.1%
5137	1	< 0.1%
5136	1	< 0.1%
5135	1	< 0.1%
5134	1	< 0.1%
5133	1	< 0.1%
5132	1	< 0.1%

Publication Type
Categorical

High correlation

Distinct	4
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	251.2 KiB

J	2665
C	2349
B	104
S	24

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	5142
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	C
2nd row	B
3rd row	C
4th row	C
5th row	C

Common Values

Value	Count	Frequency (%)
J	2665	51.8%
C	2349	45.7%
B	104	2.0%
S	24	0.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
j	2665	51.8%
c	2349	45.7%
b	104	2.0%
s	24	0.5%

Most occurring characters

Value	Count	Frequency (%)
J	2665	51.8%
C	2349	45.7%
B	104	2.0%
S	24	0.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
J	2665	51.8%
C	2349	45.7%
B	104	2.0%
S	24	0.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
J	2665	51.8%
C	2349	45.7%
B	104	2.0%
S	24	0.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
J	2665	51.8%
C	2349	45.7%
B	104	2.0%
S	24	0.5%

Authors
Text

Distinct	4729
Distinct (%)	92.0%
Missing	3
Missing (%)	0.1%
Memory size	490.9 KiB

Length

Max length	396
Median length	141
Mean length	38.615295
Min length	5

Characters and Unicode

Total characters	198444
Distinct characters	81
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4442 ?
Unique (%)	86.4%

Sample

1st row	WeiguoZou
2nd row	Repenning, A; Basawapatna, AR; Escherle, NA
3rd row	Shih, WC
4th row	Wu, SY
5th row	Lu, CJ; Zhang, S; Chen, XQ

Value	Count	Frequency (%)
m	1203	3.4%
a	1117	3.2%
j	892	2.5%
s	842	2.4%
c	722	2.1%
d	596	1.7%
r	529	1.5%
e	513	1.5%
l	475	1.4%
k	475	1.4%
Other values (8296)	27755	79.0%

Most occurring characters

Value	Count	Frequency (%)
	29980	15.1%
,	17403	8.8%
a	12744	6.4%
;	12296	6.2%
e	10033	5.1%
n	8709	4.4%
i	7779	3.9%
o	7596	3.8%
r	7412	3.7%
l	5102	2.6%
Other values (71)	79390	40.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	198444	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	29980	15.1%
,	17403	8.8%
a	12744	6.4%
;	12296	6.2%
e	10033	5.1%
n	8709	4.4%
i	7779	3.9%
o	7596	3.8%
r	7412	3.7%
l	5102	2.6%
Other values (71)	79390	40.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	198444	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	29980	15.1%
,	17403	8.8%
a	12744	6.4%
;	12296	6.2%
e	10033	5.1%
n	8709	4.4%
i	7779	3.9%
o	7596	3.8%
r	7412	3.7%
l	5102	2.6%
Other values (71)	79390	40.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	198444	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	29980	15.1%
,	17403	8.8%
a	12744	6.4%
;	12296	6.2%
e	10033	5.1%
n	8709	4.4%
i	7779	3.9%
o	7596	3.8%
r	7412	3.7%
l	5102	2.6%
Other values (71)	79390	40.0%

Book Authors
Text

Missing

Distinct	21
Distinct (%)	50.0%
Missing	5100
Missing (%)	99.2%
Memory size	162.1 KiB

Length

Max length	51
Median length	22
Mean length	14.261905
Min length	6

Characters and Unicode

Total characters	599
Distinct characters	46
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	14 ?
Unique (%)	33.3%

Sample

1st row	Anonymous
2nd row	Fonseca, D
3rd row	Felicia, P
4th row	Fonseca, D
5th row	Ros, M

Value	Count	Frequency (%)
d	12	10.4%
fonseca	11	9.6%
m	6	5.2%
ros	4	3.5%
wang	4	3.5%
ps	4	3.5%
neumann	3	2.6%
md	3	2.6%
dion	3	2.6%
l	3	2.6%
Other values (49)	62	53.9%

Most occurring characters

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Book Editors
Text

Missing

Distinct	515
Distinct (%)	38.9%
Missing	3818
Missing (%)	74.3%
Memory size	236.3 KiB

Length

Max length	455
Median length	118
Mean length	41.414653
Min length	3

Characters and Unicode

Total characters	54833
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	312 ?
Unique (%)	23.6%

Sample

1st row	Kim, Y
2nd row	Rich, PJ; Hodges, CB
3rd row	Chang, T
4th row	ACM
5th row	Blackwell, A; Plimmer, B; Stapleton, G

Value	Count	Frequency (%)
m	388	4.0%
j	335	3.5%
a	325	3.4%
s	232	2.4%
r	165	1.7%
g	153	1.6%
c	145	1.5%
t	141	1.5%
d	133	1.4%
lg	121	1.3%
Other values (1695)	7510	77.8%

Most occurring characters

Value	Count	Frequency (%)
	8324	15.2%
,	4805	8.8%
;	3517	6.4%
a	3267	6.0%
e	2789	5.1%
n	2316	4.2%
r	2312	4.2%
i	2302	4.2%
o	2281	4.2%
l	1252	2.3%
Other values (46)	21668	39.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	54833	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	8324	15.2%
,	4805	8.8%
;	3517	6.4%
a	3267	6.0%
e	2789	5.1%
n	2316	4.2%
r	2312	4.2%
i	2302	4.2%
o	2281	4.2%
l	1252	2.3%
Other values (46)	21668	39.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	54833	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	8324	15.2%
,	4805	8.8%
;	3517	6.4%
a	3267	6.0%
e	2789	5.1%
n	2316	4.2%
r	2312	4.2%
i	2302	4.2%
o	2281	4.2%
l	1252	2.3%
Other values (46)	21668	39.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	54833	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	8324	15.2%
,	4805	8.8%
;	3517	6.4%
a	3267	6.0%
e	2789	5.1%
n	2316	4.2%
r	2312	4.2%
i	2302	4.2%
o	2281	4.2%
l	1252	2.3%
Other values (46)	21668	39.5%

Book Group Authors
Categorical

High correlation Missing

Distinct	25
Distinct (%)	2.2%
Missing	4017
Missing (%)	78.1%
Memory size	282.3 KiB

IEEE	436
ACM	386
Assoc Comp Machinery	100
ASSOC COMP MACHINERY	76
ASSOC COMPUTING MACHINERY	41
Other values (20)	86

Length

Max length	41
Median length	35
Mean length	7.9093333
Min length	3

Characters and Unicode

Total characters	8898
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	0.6%

Sample

1st row	Assoc Comp Machinery
2nd row	IEEE
3rd row	ACM
4th row	IEEE
5th row	IEEE

Common Values

Value	Count	Frequency (%)
IEEE	436	8.5%
ACM	386	7.5%
Assoc Comp Machinery	100	1.9%
ASSOC COMP MACHINERY	76	1.5%
ASSOC COMPUTING MACHINERY	41	0.8%
Assoc Computing Machinery	24	0.5%
ASEE	14	0.3%
Destech Publicat Inc	10	0.2%
IEEE Comp Soc	6	0.1%
IEEE COMPUTER SOC	5	0.1%
Other values (15)	27	0.5%
(Missing)	4017	78.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ieee	448	26.7%
acm	386	23.0%
assoc	246	14.6%
machinery	242	14.4%
comp	182	10.8%
computing	66	3.9%
destech	15	0.9%
inc	15	0.9%
publicat	15	0.9%
asee	14	0.8%
Other values (21)	52	3.1%

Most occurring characters

Value	Count	Frequency (%)
E	1510	17.0%
C	888	10.0%
A	784	8.8%
M	760	8.5%
I	638	7.2%
	556	6.2%
c	319	3.6%
S	273	3.1%
s	272	3.1%
o	271	3.0%
Other values (31)	2627	29.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8898	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	1510	17.0%
C	888	10.0%
A	784	8.8%
M	760	8.5%
I	638	7.2%
	556	6.2%
c	319	3.6%
S	273	3.1%
s	272	3.1%
o	271	3.0%
Other values (31)	2627	29.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8898	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	1510	17.0%
C	888	10.0%
A	784	8.8%
M	760	8.5%
I	638	7.2%
	556	6.2%
c	319	3.6%
S	273	3.1%
s	272	3.1%
o	271	3.0%
Other values (31)	2627	29.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8898	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	1510	17.0%
C	888	10.0%
A	784	8.8%
M	760	8.5%
I	638	7.2%
	556	6.2%
c	319	3.6%
S	273	3.1%
s	272	3.1%
o	271	3.0%
Other values (31)	2627	29.5%

Author Full Names
Text

Distinct	4759
Distinct (%)	92.6%
Missing	3
Missing (%)	0.1%
Memory size	541.2 KiB

Length

Max length	568
Median length	186
Mean length	58.786729
Min length	6

Characters and Unicode

Total characters	302105
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4492 ?
Unique (%)	87.4%

Sample

1st row	WeiguoZou
2nd row	Repenning, Alexander; Basawapatna, Ashok R.; Escherle, Nora A.
3rd row	Shih, Wen-Chung
4th row	Wu, Sheng-Yi
5th row	Lu Changjin; Zhang Shuai; Chen Xiuqiong

Value	Count	Frequency (%)
m	283	0.7%
a	281	0.7%
j	217	0.5%
wang	194	0.5%
chen	177	0.4%
li	175	0.4%
c	170	0.4%
maria	170	0.4%
s	161	0.4%
zhang	157	0.4%
Other values (12983)	37970	95.0%

Most occurring characters

Value	Count	Frequency (%)
	34816	11.5%
a	28585	9.5%
e	19783	6.5%
i	19519	6.5%
n	19497	6.5%
,	17381	5.8%
r	14354	4.8%
o	14072	4.7%
;	12296	4.1%
l	9893	3.3%
Other values (52)	111909	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	302105	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	34816	11.5%
a	28585	9.5%
e	19783	6.5%
i	19519	6.5%
n	19497	6.5%
,	17381	5.8%
r	14354	4.8%
o	14072	4.7%
;	12296	4.1%
l	9893	3.3%
Other values (52)	111909	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	302105	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	34816	11.5%
a	28585	9.5%
e	19783	6.5%
i	19519	6.5%
n	19497	6.5%
,	17381	5.8%
r	14354	4.8%
o	14072	4.7%
;	12296	4.1%
l	9893	3.3%
Other values (52)	111909	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	302105	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	34816	11.5%
a	28585	9.5%
e	19783	6.5%
i	19519	6.5%
n	19497	6.5%
,	17381	5.8%
r	14354	4.8%
o	14072	4.7%
;	12296	4.1%
l	9893	3.3%
Other values (52)	111909	37.0%

Book Author Full Names
Text

Missing

Distinct	21
Distinct (%)	50.0%
Missing	5100
Missing (%)	99.2%
Memory size	162.1 KiB

Length

Max length	51
Median length	22
Mean length	14.261905
Min length	6

Characters and Unicode

Total characters	599
Distinct characters	46
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	14 ?
Unique (%)	33.3%

Sample

1st row	Anonymous
2nd row	Fonseca, D
3rd row	Felicia, P
4th row	Fonseca, D
5th row	Ros, M

Value	Count	Frequency (%)
d	12	10.4%
fonseca	11	9.6%
m	6	5.2%
ros	4	3.5%
wang	4	3.5%
ps	4	3.5%
neumann	3	2.6%
md	3	2.6%
dion	3	2.6%
l	3	2.6%
Other values (49)	62	53.9%

Most occurring characters

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	599	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	73	12.2%
,	57	9.5%
n	40	6.7%
a	36	6.0%
o	35	5.8%
e	34	5.7%
s	24	4.0%
D	21	3.5%
c	19	3.2%
r	19	3.2%
Other values (36)	241	40.2%

Group Authors
Text

Missing

Distinct	2
Distinct (%)	100.0%
Missing	5140
Missing (%)	> 99.9%
Memory size	160.9 KiB

Length

Max length	24
Median length	21
Mean length	21
Min length	18

Characters and Unicode

Total characters	42
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	CRRAFT Partnership
2nd row	DGKL Working Grp Digital

Value	Count	Frequency (%)
crraft	1	16.7%
partnership	1	16.7%
dgkl	1	16.7%
working	1	16.7%
grp	1	16.7%
digital	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
r	4	9.5%
	4	9.5%
i	4	9.5%
a	2	4.8%
R	2	4.8%
n	2	4.8%
p	2	4.8%
t	2	4.8%
D	2	4.8%
g	2	4.8%
Other values (15)	16	38.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	42	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
r	4	9.5%
	4	9.5%
i	4	9.5%
a	2	4.8%
R	2	4.8%
n	2	4.8%
p	2	4.8%
t	2	4.8%
D	2	4.8%
g	2	4.8%
Other values (15)	16	38.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	42	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
r	4	9.5%
	4	9.5%
i	4	9.5%
a	2	4.8%
R	2	4.8%
n	2	4.8%
p	2	4.8%
t	2	4.8%
D	2	4.8%
g	2	4.8%
Other values (15)	16	38.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	42	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
r	4	9.5%
	4	9.5%
i	4	9.5%
a	2	4.8%
R	2	4.8%
n	2	4.8%
p	2	4.8%
t	2	4.8%
D	2	4.8%
g	2	4.8%
Other values (15)	16	38.1%

Article Title
Text

Distinct	5128
Distinct (%)	99.7%
Missing	0
Missing (%)	0.0%
Memory size	735.5 KiB

Length

Max length	242
Median length	168
Mean length	97.060288
Min length	6

Characters and Unicode

Total characters	499084
Distinct characters	91
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5116 ?
Unique (%)	99.5%

Sample

1st row	Computational Thinking Ability Training in College Computer Teaching
2nd row	Principles of Computational Thinking Tools
3rd row	Integrating Computational Thinking into the Process of Learning Artificial Intelligence
4th row	The Development and Challenges of Computational Thinking Board Games
5th row	The Study on Computational Thinking

Value	Count	Frequency (%)
computational	2788	4.4%
thinking	2760	4.3%
of	2563	4.0%
in	2469	3.9%
and	2458	3.8%
a	1896	3.0%
the	1782	2.8%
for	1302	2.0%
learning	1095	1.7%
to	1029	1.6%
Other values (5669)	43834	68.5%

Most occurring characters

Value	Count	Frequency (%)
	58834	11.8%
n	38976	7.8%
i	38340	7.7%
e	34819	7.0%
o	31969	6.4%
t	31765	6.4%
a	31147	6.2%
r	21954	4.4%
s	18140	3.6%
l	16097	3.2%
Other values (81)	177043	35.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	499084	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	58834	11.8%
n	38976	7.8%
i	38340	7.7%
e	34819	7.0%
o	31969	6.4%
t	31765	6.4%
a	31147	6.2%
r	21954	4.4%
s	18140	3.6%
l	16097	3.2%
Other values (81)	177043	35.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	499084	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	58834	11.8%
n	38976	7.8%
i	38340	7.7%
e	34819	7.0%
o	31969	6.4%
t	31765	6.4%
a	31147	6.2%
r	21954	4.4%
s	18140	3.6%
l	16097	3.2%
Other values (81)	177043	35.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	499084	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	58834	11.8%
n	38976	7.8%
i	38340	7.7%
e	34819	7.0%
o	31969	6.4%
t	31765	6.4%
a	31147	6.2%
r	21954	4.4%
s	18140	3.6%
l	16097	3.2%
Other values (81)	177043	35.5%

Source Title
Text

Distinct	1607
Distinct (%)	31.3%
Missing	0
Missing (%)	0.0%
Memory size	528.7 KiB

Length

Max length	242
Median length	125
Mean length	56.26196
Min length	2

Characters and Unicode

Total characters	289299
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	975 ?
Unique (%)	19.0%

Sample

1st row	PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND TECHNOLOGY EDUCATION (ICSSTE 2015)
2nd row	EMERGING RESEARCH, PRACTICE, AND POLICY ON COMPUTATIONAL THINKING
3rd row	ICEMT 2019: 2019 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND MULTIMEDIA TECHNOLOGY
4th row	2018 FIRST INTERNATIONAL COGNITIVE CITIES CONFERENCE (IC3 2018)
5th row	2012 INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MANAGEMENT INNOVATION (ERMI 2012), VOL 1

Value	Count	Frequency (%)
education	2506	6.6%
of	1907	5.0%
and	1826	4.8%
on	1570	4.1%
conference	1497	3.9%
in	1256	3.3%
international	1063	2.8%
the	1020	2.7%
proceedings	973	2.6%
science	782	2.1%
Other values (1866)	23510	62.0%

Most occurring characters

Value	Count	Frequency (%)
	32768	11.3%
E	29565	10.2%
N	28186	9.7%
I	22395	7.7%
O	21399	7.4%
C	19269	6.7%
A	17467	6.0%
T	17147	5.9%
R	12556	4.3%
S	10706	3.7%
Other values (64)	77841	26.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	289299	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	32768	11.3%
E	29565	10.2%
N	28186	9.7%
I	22395	7.7%
O	21399	7.4%
C	19269	6.7%
A	17467	6.0%
T	17147	5.9%
R	12556	4.3%
S	10706	3.7%
Other values (64)	77841	26.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	289299	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	32768	11.3%
E	29565	10.2%
N	28186	9.7%
I	22395	7.7%
O	21399	7.4%
C	19269	6.7%
A	17467	6.0%
T	17147	5.9%
R	12556	4.3%
S	10706	3.7%
Other values (64)	77841	26.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	289299	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	32768	11.3%
E	29565	10.2%
N	28186	9.7%
I	22395	7.7%
O	21399	7.4%
C	19269	6.7%
A	17467	6.0%
T	17147	5.9%
R	12556	4.3%
S	10706	3.7%
Other values (64)	77841	26.9%

Book Series Title
Text

Missing

Distinct	164
Distinct (%)	13.3%
Missing	3911
Missing (%)	76.1%
Memory size	234.0 KiB

Length

Max length	108
Median length	86
Mean length	43.855402
Min length	5

Characters and Unicode

Total characters	53986
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	75 ?
Unique (%)	6.1%

Sample

1st row	Advances in Social Science Education and Humanities Research
2nd row	Educational Communications and Technology-Issues and Innovations
3rd row	International Conference on Systems and Informatics
4th row	Symposium on Visual Languages and Human Centric Computing VL HCC
5th row	Applied Mechanics and Materials

Value	Count	Frequency (%)
in	535	8.0%
conference	531	7.9%
and	384	5.7%
education	356	5.3%
on	287	4.3%
science	272	4.1%
proceedings	256	3.8%
computer	243	3.6%
notes	221	3.3%
lecture	221	3.3%
Other values (255)	3381	50.6%

Most occurring characters

Value	Count	Frequency (%)
n	6317	11.7%
e	5928	11.0%
	5456	10.1%
o	3939	7.3%
i	3647	6.8%
t	2896	5.4%
c	2795	5.2%
a	2624	4.9%
r	2592	4.8%
s	1877	3.5%
Other values (50)	15915	29.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	53986	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	6317	11.7%
e	5928	11.0%
	5456	10.1%
o	3939	7.3%
i	3647	6.8%
t	2896	5.4%
c	2795	5.2%
a	2624	4.9%
r	2592	4.8%
s	1877	3.5%
Other values (50)	15915	29.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	53986	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	6317	11.7%
e	5928	11.0%
	5456	10.1%
o	3939	7.3%
i	3647	6.8%
t	2896	5.4%
c	2795	5.2%
a	2624	4.9%
r	2592	4.8%
s	1877	3.5%
Other values (50)	15915	29.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	53986	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	6317	11.7%
e	5928	11.0%
	5456	10.1%
o	3939	7.3%
i	3647	6.8%
t	2896	5.4%
c	2795	5.2%
a	2624	4.9%
r	2592	4.8%
s	1877	3.5%
Other values (50)	15915	29.5%

Book Series Subtitle
Unsupported

Missing Rejected Unsupported

Missing	5142
Missing (%)	100.0%
Memory size	40.3 KiB

Language
Categorical

High correlation Imbalance

Distinct	12
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	281.4 KiB

English	4964
Spanish	117
Portuguese	32
Chinese	9
French	6
Other values (7)	14

Length

Max length	11
Median length	7
Mean length	7.0173084
Min length	6

Characters and Unicode

Total characters	36083
Distinct characters	30
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	< 0.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	4964	96.5%
Spanish	117	2.3%
Portuguese	32	0.6%
Chinese	9	0.2%
French	6	0.1%
Turkish	3	0.1%
German	3	0.1%
Korean	2	< 0.1%
Russian	2	< 0.1%
Arabic	2	< 0.1%
Other values (2)	2	< 0.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	4964	96.5%
spanish	117	2.3%
portuguese	32	0.6%
chinese	9	0.2%
french	6	0.1%
turkish	3	0.1%
german	3	0.1%
korean	2	< 0.1%
russian	2	< 0.1%
arabic	2	< 0.1%
Other values (2)	2	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
s	5130	14.2%
n	5106	14.2%
i	5101	14.1%
h	5099	14.1%
g	4996	13.8%
E	4964	13.8%
l	4964	13.8%
a	128	0.4%
p	118	0.3%
S	117	0.3%
Other values (20)	360	1.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	36083	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
s	5130	14.2%
n	5106	14.2%
i	5101	14.1%
h	5099	14.1%
g	4996	13.8%
E	4964	13.8%
l	4964	13.8%
a	128	0.4%
p	118	0.3%
S	117	0.3%
Other values (20)	360	1.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	36083	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
s	5130	14.2%
n	5106	14.2%
i	5101	14.1%
h	5099	14.1%
g	4996	13.8%
E	4964	13.8%
l	4964	13.8%
a	128	0.4%
p	118	0.3%
S	117	0.3%
Other values (20)	360	1.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	36083	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
s	5130	14.2%
n	5106	14.2%
i	5101	14.1%
h	5099	14.1%
g	4996	13.8%
E	4964	13.8%
l	4964	13.8%
a	128	0.4%
p	118	0.3%
S	117	0.3%
Other values (20)	360	1.0%

Document Type
Categorical

High correlation Imbalance

Distinct	19
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	308.2 KiB

Proceedings Paper	2349
Article	2258
Review	196
Article; Early Access	95
Article; Book Chapter	90
Other values (14)	154

Length

Max length	32
Median length	30
Mean length	12.345974
Min length	4

Characters and Unicode

Total characters	63483
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	0.1%

Sample

1st row	Proceedings Paper
2nd row	Article; Book Chapter
3rd row	Proceedings Paper
4th row	Proceedings Paper
5th row	Proceedings Paper

Common Values

Value	Count	Frequency (%)
Proceedings Paper	2349	45.7%
Article	2258	43.9%
Review	196	3.8%
Article; Early Access	95	1.8%
Article; Book Chapter	90	1.8%
Editorial Material	56	1.1%
Meeting Abstract	29	0.6%
Article; Proceedings Paper	15	0.3%
Correction	13	0.3%
Book Review	9	0.2%
Other values (9)	32	0.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
article	2459	30.6%
proceedings	2364	29.4%
paper	2364	29.4%
review	214	2.7%
book	113	1.4%
early	102	1.3%
access	102	1.3%
chapter	101	1.3%
editorial	66	0.8%
material	66	0.8%
Other values (8)	83	1.0%

Most occurring characters

Value	Count	Frequency (%)
e	10339	16.3%
r	7588	12.0%
i	5280	8.3%
c	5071	8.0%
P	4729	7.4%
	2892	4.6%
t	2813	4.4%
a	2797	4.4%
l	2694	4.2%
o	2684	4.2%
Other values (22)	16596	26.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	63483	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	10339	16.3%
r	7588	12.0%
i	5280	8.3%
c	5071	8.0%
P	4729	7.4%
	2892	4.6%
t	2813	4.4%
a	2797	4.4%
l	2694	4.2%
o	2684	4.2%
Other values (22)	16596	26.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	63483	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	10339	16.3%
r	7588	12.0%
i	5280	8.3%
c	5071	8.0%
P	4729	7.4%
	2892	4.6%
t	2813	4.4%
a	2797	4.4%
l	2694	4.2%
o	2684	4.2%
Other values (22)	16596	26.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	63483	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	10339	16.3%
r	7588	12.0%
i	5280	8.3%
c	5071	8.0%
P	4729	7.4%
	2892	4.6%
t	2813	4.4%
a	2797	4.4%
l	2694	4.2%
o	2684	4.2%
Other values (22)	16596	26.1%

Conference Title
Text

Missing

Distinct	916
Distinct (%)	38.3%
Missing	2751
Missing (%)	53.5%
Memory size	375.5 KiB

Length

Max length	239
Median length	168
Mean length	74.922208
Min length	12

Characters and Unicode

Total characters	179139
Distinct characters	73
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	583 ?
Unique (%)	24.4%

Sample

1st row	International Conference on Social Science and Technology Education (ICSSTE)
2nd row	3rd International Conference on Education and Multimedia Technology (ICEMT)
3rd row	1st International Cognitive Cities Conference (IC3)
4th row	International Conference on Education Reform and Management Innovation (ERMI 2012)
5th row	15th International Conference on Education Technology and Computers (ICETC)

Value	Count	Frequency (%)
conference	1920	8.6%
on	1888	8.4%
education	1320	5.9%
international	1222	5.5%
and	1124	5.0%
in	686	3.1%
computer	483	2.2%
science	467	2.1%
ieee	438	2.0%
symposium	385	1.7%
Other values (1245)	12465	55.7%

Most occurring characters

Value	Count	Frequency (%)
n	20359	11.4%
	20007	11.2%
e	14440	8.1%
o	12448	6.9%
t	9789	5.5%
i	9669	5.4%
a	9313	5.2%
c	7231	4.0%
r	7127	4.0%
C	5723	3.2%
Other values (63)	63033	35.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	179139	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	20359	11.4%
	20007	11.2%
e	14440	8.1%
o	12448	6.9%
t	9789	5.5%
i	9669	5.4%
a	9313	5.2%
c	7231	4.0%
r	7127	4.0%
C	5723	3.2%
Other values (63)	63033	35.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	179139	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	20359	11.4%
	20007	11.2%
e	14440	8.1%
o	12448	6.9%
t	9789	5.5%
i	9669	5.4%
a	9313	5.2%
c	7231	4.0%
r	7127	4.0%
C	5723	3.2%
Other values (63)	63033	35.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	179139	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	20359	11.4%
	20007	11.2%
e	14440	8.1%
o	12448	6.9%
t	9789	5.5%
i	9669	5.4%
a	9313	5.2%
c	7231	4.0%
r	7127	4.0%
C	5723	3.2%
Other values (63)	63033	35.2%

Conference Date
Text

Missing

Distinct	882
Distinct (%)	36.9%
Missing	2751
Missing (%)	53.5%
Memory size	236.1 KiB

Length

Max length	30
Median length	15
Mean length	15.220828
Min length	4

Characters and Unicode

Total characters	36393
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	512 ?
Unique (%)	21.4%

Sample

1st row	APR 11-12, 2015
2nd row	JUL 22-25, 2019
3rd row	AUG 07-09, 2018
4th row	DEC 04-05, 2012
5th row	SEP 26-28, 2023

Value	Count	Frequency (%)
oct	405	5.5%
2019	317	4.3%
jul	315	4.3%
mar	315	4.3%
2018	275	3.7%
nov	241	3.3%
2017	240	3.3%
2021	239	3.3%
jun	235	3.2%
2020	229	3.1%
Other values (230)	4527	61.7%

Most occurring characters

Value	Count	Frequency (%)
2	5750	15.8%
	4947	13.6%
0	4409	12.1%
1	3790	10.4%
,	2382	6.5%
-	2344	6.4%
3	891	2.4%
A	840	2.3%
8	813	2.2%
U	736	2.0%
Other values (22)	9491	26.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	36393	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	5750	15.8%
	4947	13.6%
0	4409	12.1%
1	3790	10.4%
,	2382	6.5%
-	2344	6.4%
3	891	2.4%
A	840	2.3%
8	813	2.2%
U	736	2.0%
Other values (22)	9491	26.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	36393	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	5750	15.8%
	4947	13.6%
0	4409	12.1%
1	3790	10.4%
,	2382	6.5%
-	2344	6.4%
3	891	2.4%
A	840	2.3%
8	813	2.2%
U	736	2.0%
Other values (22)	9491	26.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	36393	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	5750	15.8%
	4947	13.6%
0	4409	12.1%
1	3790	10.4%
,	2382	6.5%
-	2344	6.4%
3	891	2.4%
A	840	2.3%
8	813	2.2%
U	736	2.0%
Other values (22)	9491	26.1%

Conference Location
Text

Missing

Distinct	565
Distinct (%)	23.6%
Missing	2751
Missing (%)	53.5%
Memory size	248.0 KiB

Length

Max length	94
Median length	76
Mean length	20.333752
Min length	2

Characters and Unicode

Total characters	48618
Distinct characters	58
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	272 ?
Unique (%)	11.4%

Sample

1st row	Sanya, PEOPLES R CHINA
2nd row	Nagoya, JAPAN
3rd row	Okinawa, JAPAN
4th row	Shenzhen, PEOPLES R CHINA
5th row	Univ Barcelona, Barcelona, SPAIN

Value	Count	Frequency (%)
univ	429	5.9%
network	272	3.8%
electr	272	3.8%
spain	219	3.0%
hong	127	1.8%
kong	127	1.8%
china	122	1.7%
r	112	1.6%
peoples	112	1.6%
england	73	1.0%
Other values (817)	5354	74.2%

Most occurring characters

Value	Count	Frequency (%)
	4828	9.9%
n	2706	5.6%
,	2630	5.4%
a	2546	5.2%
A	2192	4.5%
i	2018	4.2%
E	1959	4.0%
N	1890	3.9%
e	1823	3.7%
o	1815	3.7%
Other values (48)	24211	49.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	48618	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4828	9.9%
n	2706	5.6%
,	2630	5.4%
a	2546	5.2%
A	2192	4.5%
i	2018	4.2%
E	1959	4.0%
N	1890	3.9%
e	1823	3.7%
o	1815	3.7%
Other values (48)	24211	49.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	48618	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4828	9.9%
n	2706	5.6%
,	2630	5.4%
a	2546	5.2%
A	2192	4.5%
i	2018	4.2%
E	1959	4.0%
N	1890	3.9%
e	1823	3.7%
o	1815	3.7%
Other values (48)	24211	49.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	48618	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4828	9.9%
n	2706	5.6%
,	2630	5.4%
a	2546	5.2%
A	2192	4.5%
i	2018	4.2%
E	1959	4.0%
N	1890	3.9%
e	1823	3.7%
o	1815	3.7%
Other values (48)	24211	49.8%

Conference Sponsor
Text

Missing

Distinct	531
Distinct (%)	31.4%
Missing	3453
Missing (%)	67.2%
Memory size	328.7 KiB

Length

Max length	1103
Median length	272
Mean length	84.797513
Min length	3

Characters and Unicode

Total characters	143223
Distinct characters	71
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	317 ?
Unique (%)	18.8%

Sample

1st row	Int Assoc Cyber Sci & Engn
2nd row	IEEE Comp Soc,Okinawa Inst Sci & Technol
3rd row	Grup Recerca Ensenyament Aprenentatge Virtual,Univ Warwick,Tecnologico Monterrey
4th row	Assoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Comp Sci Educ,ACM Europe Council,Informat Europe,Github Educ,Univ Milano, Dipartimento Informatica
5th row	Shanghai Dianji Univ, Sch Elect & Informat,IEEE Syst, Man, & Cybernet Soc

Value	Count	Frequency (%)
comp	1331	7.4%
educ	730	4.1%
	707	3.9%
sci	507	2.8%
univ	475	2.6%
assoc	474	2.6%
soc	380	2.1%
grp	345	1.9%
special	317	1.8%
res	317	1.8%
Other values (2441)	12358	68.9%

Most occurring characters

Value	Count	Frequency (%)
	16252	11.3%
n	8707	6.1%
e	8403	5.9%
o	7992	5.6%
i	7529	5.3%
c	7406	5.2%
a	7027	4.9%
E	6306	4.4%
,	6097	4.3%
t	5726	4.0%
Other values (61)	61778	43.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	143223	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	16252	11.3%
n	8707	6.1%
e	8403	5.9%
o	7992	5.6%
i	7529	5.3%
c	7406	5.2%
a	7027	4.9%
E	6306	4.4%
,	6097	4.3%
t	5726	4.0%
Other values (61)	61778	43.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	143223	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	16252	11.3%
n	8707	6.1%
e	8403	5.9%
o	7992	5.6%
i	7529	5.3%
c	7406	5.2%
a	7027	4.9%
E	6306	4.4%
,	6097	4.3%
t	5726	4.0%
Other values (61)	61778	43.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	143223	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	16252	11.3%
n	8707	6.1%
e	8403	5.9%
o	7992	5.6%
i	7529	5.3%
c	7406	5.2%
a	7027	4.9%
E	6306	4.4%
,	6097	4.3%
t	5726	4.0%
Other values (61)	61778	43.1%

Conference Host
Text

Missing

Distinct	198
Distinct (%)	37.9%
Missing	4619
Missing (%)	89.8%
Memory size	180.7 KiB

Length

Max length	74
Median length	48
Mean length	21.839388
Min length	4

Characters and Unicode

Total characters	11422
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	112 ?
Unique (%)	21.4%

Sample

1st row	Univ Barcelona
2nd row	Univ Milano
3rd row	TU Wien
4th row	Univ Turku
5th row	City Univ Hong Kong

Value	Count	Frequency (%)
univ	429	22.5%
technol	46	2.4%
sci	45	2.4%
educ	45	2.4%
kong	40	2.1%
hong	40	2.1%
city	35	1.8%
	35	1.8%
fac	27	1.4%
inst	27	1.4%
Other values (338)	1135	59.6%

Most occurring characters

Value	Count	Frequency (%)
	1381	12.1%
n	1223	10.7%
i	966	8.5%
a	655	5.7%
e	628	5.5%
o	550	4.8%
t	483	4.2%
v	467	4.1%
U	452	4.0%
r	390	3.4%
Other values (46)	4227	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11422	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1381	12.1%
n	1223	10.7%
i	966	8.5%
a	655	5.7%
e	628	5.5%
o	550	4.8%
t	483	4.2%
v	467	4.1%
U	452	4.0%
r	390	3.4%
Other values (46)	4227	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11422	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1381	12.1%
n	1223	10.7%
i	966	8.5%
a	655	5.7%
e	628	5.5%
o	550	4.8%
t	483	4.2%
v	467	4.1%
U	452	4.0%
r	390	3.4%
Other values (46)	4227	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11422	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1381	12.1%
n	1223	10.7%
i	966	8.5%
a	655	5.7%
e	628	5.5%
o	550	4.8%
t	483	4.2%
v	467	4.1%
U	452	4.0%
r	390	3.4%
Other values (46)	4227	37.0%

Author Keywords
Text

Missing

Distinct	4644
Distinct (%)	99.1%
Missing	457
Missing (%)	8.9%
Memory size	682.9 KiB

Length

Max length	871
Median length	216
Mean length	96.636286
Min length	3

Characters and Unicode

Total characters	452741
Distinct characters	81
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4609 ?
Unique (%)	98.4%

Sample

1st row	Computational Thinking; Computer Teaching; Blended Teaching Model
2nd row	Computational Thinking Process; Three stages of the Computational Thinking Process; Computational Thinking Tools; Principles of Computational Thinking Tools
3rd row	Computational thinking; Experiential learning; Artificial intelligence
4th row	computational thinking; board game; coding; game-based learning
5th row	information process; computational thinking; talent-training strategy

Value	Count	Frequency (%)
thinking	3724	8.1%
computational	3617	7.9%
education	2300	5.0%
learning	1582	3.4%
programming	1408	3.1%
science	776	1.7%
computer	706	1.5%
educational	478	1.0%
design	445	1.0%
robotics	396	0.9%
Other values (4045)	30429	66.4%

Most occurring characters

Value	Count	Frequency (%)
	41176	9.1%
i	39793	8.8%
n	36467	8.1%
t	32265	7.1%
e	31673	7.0%
a	31628	7.0%
o	29098	6.4%
r	19823	4.4%
c	18925	4.2%
;	18830	4.2%
Other values (71)	153063	33.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	452741	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	41176	9.1%
i	39793	8.8%
n	36467	8.1%
t	32265	7.1%
e	31673	7.0%
a	31628	7.0%
o	29098	6.4%
r	19823	4.4%
c	18925	4.2%
;	18830	4.2%
Other values (71)	153063	33.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	452741	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	41176	9.1%
i	39793	8.8%
n	36467	8.1%
t	32265	7.1%
e	31673	7.0%
a	31628	7.0%
o	29098	6.4%
r	19823	4.4%
c	18925	4.2%
;	18830	4.2%
Other values (71)	153063	33.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	452741	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	41176	9.1%
i	39793	8.8%
n	36467	8.1%
t	32265	7.1%
e	31673	7.0%
a	31628	7.0%
o	29098	6.4%
r	19823	4.4%
c	18925	4.2%
;	18830	4.2%
Other values (71)	153063	33.8%

Keywords Plus
Text

Missing

Distinct	2145
Distinct (%)	77.3%
Missing	2366
Missing (%)	46.0%
Memory size	332.0 KiB

Length

Max length	187
Median length	144
Mean length	46.144452
Min length	2

Characters and Unicode

Total characters	128097
Distinct characters	40
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2008 ?
Unique (%)	72.3%

Sample

1st row	END
2nd row	ELEMENTARY CLASSROOMS; COMPUTER-SCIENCE; ROBOTICS; SKILLS; PAPER; EXPLORATION; VIEWPOINT; EDUCATION; VALIDITY; DESIGN
3rd row	YOUTH; CURRICULUM
4th row	VALIDITY; K-12
5th row	MATHEMATICS; LANGUAGE

Value	Count	Frequency (%)
thinking	921	7.3%
computational	820	6.5%
education	470	3.7%
students	352	2.8%
science	343	2.7%
robotics	327	2.6%
k-12	311	2.5%
design	304	2.4%
skills	300	2.4%
mathematics	220	1.7%
Other values (1449)	8241	65.4%

Most occurring characters

Value	Count	Frequency (%)
E	11315	8.8%
I	10819	8.4%
T	10346	8.1%
	9833	7.7%
N	9272	7.2%
A	8032	6.3%
O	7997	6.2%
;	7636	6.0%
C	7070	5.5%
S	6921	5.4%
Other values (30)	38856	30.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	128097	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	11315	8.8%
I	10819	8.4%
T	10346	8.1%
	9833	7.7%
N	9272	7.2%
A	8032	6.3%
O	7997	6.2%
;	7636	6.0%
C	7070	5.5%
S	6921	5.4%
Other values (30)	38856	30.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	128097	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	11315	8.8%
I	10819	8.4%
T	10346	8.1%
	9833	7.7%
N	9272	7.2%
A	8032	6.3%
O	7997	6.2%
;	7636	6.0%
C	7070	5.5%
S	6921	5.4%
Other values (30)	38856	30.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	128097	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	11315	8.8%
I	10819	8.4%
T	10346	8.1%
	9833	7.7%
N	9272	7.2%
A	8032	6.3%
O	7997	6.2%
;	7636	6.0%
C	7070	5.5%
S	6921	5.4%
Other values (30)	38856	30.3%

Abstract
Text

Missing

Distinct	4971
Distinct (%)	99.9%
Missing	164
Missing (%)	3.2%
Memory size	6.3 MiB

Length

Max length	22855
Median length	1782
Mean length	1283.3435
Min length	81

Characters and Unicode

Total characters	6388484
Distinct characters	92
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4964 ?
Unique (%)	99.7%

Sample

1st row	Computational thinking is ubiquitous, and has become a hot spot in education. As the core mission of college computer teaching, computational thinking training naturally causes widespread concern in the field of basic computer education. In this study, we propose the computational thinking formation procedural model, which reveals that computational thinking is the unity of internal and process, and the computational thinking ability training process is an important reason for the formation of computational thinking ability. And based on the factors of computational thinking training process, we construct the computational thinking - based blended teaching model.
2nd row	Computational Thinking is a fundamental skill for the twenty-first century workforce. This broad target audience, including teachers and students with no programming experience, necessitates a shift in perspective toward Computational Thinking Tools that not only provide highly accessible programming environments but explicitly support the Computational Thinking Process. This evolution is crucial if Computational Thinking Tools are to be relevant to a wide range of school disciplines including STEM, art, music, and language learning. Computational Thinking Tools must help users through three fundamental stages of Computational Thinking: problem formulation, solution expression, and execution/evaluation. This chapter outlines three principles, and employs AgentCubes online as an example, on how a Computational Thinking Tool provides support for these stages by unifying human abilities with computer affordances.
3rd row	In recent years, computational thinking has once again received attention widely. Computational thinking is generally considered to be the ability to be acquired. However, this study is to use computational thinking as part of the learning method. In order to explore the application of computational thinking in teaching, this study first collected the main review papers, as well as the literature on the assessment of computational thinking, and examined their views. Then, this study proposes a learning method that integrates computational thinking into experiential learning theory and applies it to learning artificial intelligence techniques.
4th row	The promotion of computational thinking education has become a worldwide trend. To cultivate the computational thinking ability of children at young age, many computational thinking board games have appeared recently. This article introduces five computational thinking board games, including Robot Turtles, King of Pirates, Doggy Code, ROBOT WARS Coding Board Game, and Code master, and then to analyze its characteristics respectively. Additionally, this article also points out the current limitations and challenges of computational thinking board games. We hope more schools or operators will join the development of computational thinking education in the future.
5th row	This article makes an introduction to the research of computational thinking on its form, characteristic and present situation, analyses the background and the reasons for the rise of computational thinking, draws the conclusion that computational thinking should be considered from national strategic level of talent-training, and proposes some countermeasures of computational thinking enhancement, talent-training and information process.

Value	Count	Frequency (%)
the	51301	5.6%
and	36949	4.0%
of	32708	3.6%
to	25008	2.7%
in	24936	2.7%
a	17824	2.0%
students	10328	1.1%
for	9945	1.1%
thinking	9300	1.0%
this	8956	1.0%
Other values (24207)	686033	75.1%

Most occurring characters

Value	Count	Frequency (%)
	908321	14.2%
e	616165	9.6%
t	477621	7.5%
i	440803	6.9%
n	421788	6.6%
a	403329	6.3%
o	377772	5.9%
s	354076	5.5%
r	312893	4.9%
c	225762	3.5%
Other values (82)	1849954	29.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6388484	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	908321	14.2%
e	616165	9.6%
t	477621	7.5%
i	440803	6.9%
n	421788	6.6%
a	403329	6.3%
o	377772	5.9%
s	354076	5.5%
r	312893	4.9%
c	225762	3.5%
Other values (82)	1849954	29.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6388484	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	908321	14.2%
e	616165	9.6%
t	477621	7.5%
i	440803	6.9%
n	421788	6.6%
a	403329	6.3%
o	377772	5.9%
s	354076	5.5%
r	312893	4.9%
c	225762	3.5%
Other values (82)	1849954	29.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6388484	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	908321	14.2%
e	616165	9.6%
t	477621	7.5%
i	440803	6.9%
n	421788	6.6%
a	403329	6.3%
o	377772	5.9%
s	354076	5.5%
r	312893	4.9%
c	225762	3.5%
Other values (82)	1849954	29.0%

Addresses
Text

Distinct	5014
Distinct (%)	98.1%
Missing	30
Missing (%)	0.6%
Memory size	1.2 MiB

Length

Max length	1245
Median length	485
Mean length	193.00528
Min length	28

Characters and Unicode

Total characters	986643
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4933 ?
Unique (%)	96.5%

Sample

1st row	Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China
2nd row	[Repenning, Alexander; Escherle, Nora A.] Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland; [Basawapatna, Ashok R.] SUNY Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USA
3rd row	[Shih, Wen-Chung] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan
4th row	[Wu, Sheng-Yi] Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan
5th row	[Lu Changjin; Zhang Shuai; Chen Xiuqiong] Sanming Univ, Dept Math & Comp Sci, Sanming, Fujian Province, Peoples R China

Value	Count	Frequency (%)
univ	8498	5.9%
educ	3383	2.4%
	3247	2.3%
usa	2964	2.1%
dept	2933	2.0%
sci	2181	1.5%
technol	1594	1.1%
comp	1395	1.0%
china	1368	1.0%
sch	1365	0.9%
Other values (20985)	114810	79.9%

Most occurring characters

Value	Count	Frequency (%)
	138626	14.1%
a	72666	7.4%
n	63274	6.4%
i	56418	5.7%
e	54238	5.5%
,	51159	5.2%
o	40778	4.1%
r	35440	3.6%
l	31065	3.1%
t	29340	3.0%
Other values (64)	413639	41.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	986643	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	138626	14.1%
a	72666	7.4%
n	63274	6.4%
i	56418	5.7%
e	54238	5.5%
,	51159	5.2%
o	40778	4.1%
r	35440	3.6%
l	31065	3.1%
t	29340	3.0%
Other values (64)	413639	41.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	986643	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	138626	14.1%
a	72666	7.4%
n	63274	6.4%
i	56418	5.7%
e	54238	5.5%
,	51159	5.2%
o	40778	4.1%
r	35440	3.6%
l	31065	3.1%
t	29340	3.0%
Other values (64)	413639	41.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	986643	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	138626	14.1%
a	72666	7.4%
n	63274	6.4%
i	56418	5.7%
e	54238	5.5%
,	51159	5.2%
o	40778	4.1%
r	35440	3.6%
l	31065	3.1%
t	29340	3.0%
Other values (64)	413639	41.9%

Affiliations
Text

Missing

Distinct	2946
Distinct (%)	60.7%
Missing	290
Missing (%)	5.6%
Memory size	553.5 KiB

Length

Max length	725
Median length	277
Mean length	65.883141
Min length	5

Characters and Unicode

Total characters	319665
Distinct characters	67
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2239 ?
Unique (%)	46.1%

Sample

1st row	FHNW University of Applied Sciences & Arts Northwestern Switzerland; State University of New York (SUNY) System; SUNY Old Westbury
2nd row	Asia University Taiwan
3rd row	National Pingtung University
4th row	Sanming University
5th row	Central China Normal University; Zhongnan University of Economics & Law

Value	Count	Frequency (%)
university	7968	19.6%
of	4565	11.2%
system	1195	2.9%
de	1011	2.5%
state	863	2.1%
universidad	646	1.6%
technology	615	1.5%
national	538	1.3%
	511	1.3%
normal	427	1.1%
Other values (2213)	22277	54.8%

Most occurring characters

Value	Count	Frequency (%)
	35764	11.2%
i	31015	9.7%
e	25491	8.0%
n	23036	7.2%
a	19425	6.1%
t	19410	6.1%
r	17039	5.3%
o	16905	5.3%
s	16473	5.2%
y	11380	3.6%
Other values (57)	103727	32.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	319665	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	35764	11.2%
i	31015	9.7%
e	25491	8.0%
n	23036	7.2%
a	19425	6.1%
t	19410	6.1%
r	17039	5.3%
o	16905	5.3%
s	16473	5.2%
y	11380	3.6%
Other values (57)	103727	32.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	319665	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	35764	11.2%
i	31015	9.7%
e	25491	8.0%
n	23036	7.2%
a	19425	6.1%
t	19410	6.1%
r	17039	5.3%
o	16905	5.3%
s	16473	5.2%
y	11380	3.6%
Other values (57)	103727	32.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	319665	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	35764	11.2%
i	31015	9.7%
e	25491	8.0%
n	23036	7.2%
a	19425	6.1%
t	19410	6.1%
r	17039	5.3%
o	16905	5.3%
s	16473	5.2%
y	11380	3.6%
Other values (57)	103727	32.4%

Reprint Addresses
Text

Missing

Distinct	4614
Distinct (%)	91.4%
Missing	92
Missing (%)	1.8%
Memory size	825.3 KiB

Length

Max length	457
Median length	309
Mean length	108.82515
Min length	53

Characters and Unicode

Total characters	549567
Distinct characters	90
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4297 ?
Unique (%)	85.1%

Sample

1st row	WeiguoZou (autor correspondiente), Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China.
2nd row	Repenning, A (autor correspondiente), Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland.
3rd row	Shih, WC (autor correspondiente), Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.
4th row	Wu, SY (autor correspondiente), Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan.
5th row	Song, LL (autor correspondiente), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.

Value	Count	Frequency (%)
autor	5466	7.1%
correspondiente	5466	7.1%
univ	4623	6.0%
educ	1854	2.4%
	1746	2.3%
dept	1583	2.1%
usa	1334	1.7%
sci	1178	1.5%
technol	894	1.2%
r	860	1.1%
Other values (9706)	52044	67.5%

Most occurring characters

Value	Count	Frequency (%)
	71998	13.1%
e	37359	6.8%
n	36595	6.7%
o	32759	6.0%
a	32347	5.9%
r	29615	5.4%
,	28712	5.2%
i	27120	4.9%
t	23983	4.4%
c	15332	2.8%
Other values (80)	213747	38.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	549567	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	71998	13.1%
e	37359	6.8%
n	36595	6.7%
o	32759	6.0%
a	32347	5.9%
r	29615	5.4%
,	28712	5.2%
i	27120	4.9%
t	23983	4.4%
c	15332	2.8%
Other values (80)	213747	38.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	549567	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	71998	13.1%
e	37359	6.8%
n	36595	6.7%
o	32759	6.0%
a	32347	5.9%
r	29615	5.4%
,	28712	5.2%
i	27120	4.9%
t	23983	4.4%
c	15332	2.8%
Other values (80)	213747	38.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	549567	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	71998	13.1%
e	37359	6.8%
n	36595	6.7%
o	32759	6.0%
a	32347	5.9%
r	29615	5.4%
,	28712	5.2%
i	27120	4.9%
t	23983	4.4%
c	15332	2.8%
Other values (80)	213747	38.9%

Email Addresses
Text

Missing

Distinct	4153
Distinct (%)	88.6%
Missing	454
Missing (%)	8.8%
Memory size	491.5 KiB

Length

Max length	726
Median length	224
Mean length	55.236561
Min length	10

Characters and Unicode

Total characters	258949
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3808 ?
Unique (%)	81.2%

Sample

1st row	alexander.repenning@fhnw.ch; basawapatnaa@oldwestbury.edu; nora.escherle@fhnw.ch
2nd row	wjshih@asia.edu.tw
3rd row	digschool@gmail.com
4th row	smlcj123@163.com
5th row	zwccnu@ccnu.edu.cn; linglingsong@mails.ccnu.edu.cn; hxj168168@zuel.edu.cn; wlqyy_fam@163.com

Value	Count	Frequency (%)
sckong@eduhk.hk	28	0.2%
grex@gsyc.urjc.es	27	0.2%
mroman@edu.uned.es	26	0.2%
dmfranklin@uchicago.edu	23	0.2%
fgarcia@usal.es	21	0.2%
jesus.moreno@programamos.es	20	0.2%
bwmott@ncsu.edu	19	0.2%
ayadav@msu.edu	19	0.2%
misrael@coe.ufl.edu	18	0.2%
wongkwg@hku.hk	17	0.1%
Other values (7914)	11512	98.1%

Most occurring characters

Value	Count	Frequency (%)
.	20964	8.1%
e	19980	7.7%
a	19952	7.7%
u	16960	6.5%
i	14648	5.7%
n	13614	5.3%
@	11731	4.5%
o	11267	4.4%
c	11123	4.3%
r	10954	4.2%
Other values (59)	107756	41.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	258949	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	20964	8.1%
e	19980	7.7%
a	19952	7.7%
u	16960	6.5%
i	14648	5.7%
n	13614	5.3%
@	11731	4.5%
o	11267	4.4%
c	11123	4.3%
r	10954	4.2%
Other values (59)	107756	41.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	258949	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	20964	8.1%
e	19980	7.7%
a	19952	7.7%
u	16960	6.5%
i	14648	5.7%
n	13614	5.3%
@	11731	4.5%
o	11267	4.4%
c	11123	4.3%
r	10954	4.2%
Other values (59)	107756	41.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	258949	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	20964	8.1%
e	19980	7.7%
a	19952	7.7%
u	16960	6.5%
i	14648	5.7%
n	13614	5.3%
@	11731	4.5%
o	11267	4.4%
c	11123	4.3%
r	10954	4.2%
Other values (59)	107756	41.6%

Researcher Ids
Text

Missing

Distinct	2728
Distinct (%)	81.5%
Missing	1794
Missing (%)	34.9%
Memory size	463.6 KiB

Length

Max length	503
Median length	201
Mean length	53.68399
Min length	18

Characters and Unicode

Total characters	179734
Distinct characters	155
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2367 ?
Unique (%)	70.7%

Sample

1st row	Wu, Sheng-Yi/C-4143-2011
2nd row	Leung, Jessica Shuk Ching/G-4619-2013; Ezeamuzie, Ndudi Okechukwu/ABG-1289-2021
3rd row	Bower, Matt/J-7574-2016; Arguel, Amaël/H-4424-2019; Boom, Kay-Dennis/R-5200-2018
4th row	deng, wenbo/AAI-3947-2020
5th row	Liu, Shuai/P-3939-2017; Srivastava, Gautam/N-5668-2019

Value	Count	Frequency (%)
	758	5.5%
chen	50	0.4%
li	48	0.3%
de	42	0.3%
wong	40	0.3%
wang	40	0.3%
liu	39	0.3%
yang	38	0.3%
hsu	36	0.3%
marcos/c-5705-2013	33	0.2%
Other values (5933)	12617	91.8%

Most occurring characters

Value	Count	Frequency (%)
2	12812	7.1%
-	12632	7.0%
	10393	5.8%
a	9671	5.4%
0	8919	5.0%
e	6425	3.6%
n	6140	3.4%
i	6132	3.4%
/	5888	3.3%
,	5887	3.3%
Other values (145)	94835	52.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	179734	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	12812	7.1%
-	12632	7.0%
	10393	5.8%
a	9671	5.4%
0	8919	5.0%
e	6425	3.6%
n	6140	3.4%
i	6132	3.4%
/	5888	3.3%
,	5887	3.3%
Other values (145)	94835	52.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	179734	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	12812	7.1%
-	12632	7.0%
	10393	5.8%
a	9671	5.4%
0	8919	5.0%
e	6425	3.6%
n	6140	3.4%
i	6132	3.4%
/	5888	3.3%
,	5887	3.3%
Other values (145)	94835	52.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	179734	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	12812	7.1%
-	12632	7.0%
	10393	5.8%
a	9671	5.4%
0	8919	5.0%
e	6425	3.6%
n	6140	3.4%
i	6132	3.4%
/	5888	3.3%
,	5887	3.3%
Other values (145)	94835	52.8%

ORCIDs
Text

Missing

Distinct	2942
Distinct (%)	87.4%
Missing	1775
Missing (%)	34.5%
Memory size	544.3 KiB

Length

Max length	438
Median length	238
Mean length	73.659341
Min length	24

Characters and Unicode

Total characters	248011
Distinct characters	164
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2662 ?
Unique (%)	79.1%

Sample

1st row	REPENNING, ALEXANDER/0000-0002-2165-7533
2nd row	Shih, Wen-Chung/0000-0003-4838-8473
3rd row	Wu, Sheng-Yi/0000-0003-3022-1843
4th row	Benke, Gertraud/0000-0002-6710-191X
5th row	REPENNING, ALEXANDER/0000-0002-2165-7533

Value	Count	Frequency (%)
	64	0.4%
lee	57	0.4%
li	52	0.3%
liu	50	0.3%
yang	46	0.3%
chen	44	0.3%
sun	43	0.3%
wong	40	0.3%
wang	39	0.3%
de	39	0.3%
Other values (7635)	14622	96.9%

Most occurring characters

Value	Count	Frequency (%)
0	51465	20.8%
-	20684	8.3%
	11729	4.7%
a	10762	4.3%
2	8446	3.4%
e	7591	3.1%
i	7104	2.9%
n	7091	2.9%
1	6945	2.8%
/	6631	2.7%
Other values (154)	109563	44.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	248011	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	51465	20.8%
-	20684	8.3%
	11729	4.7%
a	10762	4.3%
2	8446	3.4%
e	7591	3.1%
i	7104	2.9%
n	7091	2.9%
1	6945	2.8%
/	6631	2.7%
Other values (154)	109563	44.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	248011	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	51465	20.8%
-	20684	8.3%
	11729	4.7%
a	10762	4.3%
2	8446	3.4%
e	7591	3.1%
i	7104	2.9%
n	7091	2.9%
1	6945	2.8%
/	6631	2.7%
Other values (154)	109563	44.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	248011	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	51465	20.8%
-	20684	8.3%
	11729	4.7%
a	10762	4.3%
2	8446	3.4%
e	7591	3.1%
i	7104	2.9%
n	7091	2.9%
1	6945	2.8%
/	6631	2.7%
Other values (154)	109563	44.2%

Funding Orgs
Text

Missing

Distinct	2081
Distinct (%)	89.6%
Missing	2820
Missing (%)	54.8%
Memory size	463.5 KiB

Length

Max length	943
Median length	356
Mean length	116.06202
Min length	4

Characters and Unicode

Total characters	269496
Distinct characters	86
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1942 ?
Unique (%)	83.6%

Sample

1st row	Ministry of Science and Technology of Republic of China [MOST 106-2511-S-468 -005 -MY2]
2nd row	National Natural Science Foundation of China [61977031]
3rd row	Second Round of Research Projects for Shanghai Private Colleges [2016-SHNGE-08ZD]
4th row	Hasler Foundation; National Science Foundation [0833612, 1345523, 0848962]; Direct For Education and Human Resources; Division Of Research On Learning [0833612] Funding Source: National Science Foundation; Directorate For Engineering [0848962, 1345523] Funding Source: National Science Foundation; Div Of Industrial Innovation & Partnersh [1345523, 0848962] Funding Source: National Science Foundation
5th row	Institute of Museum and Library Services, USA [LG-14-19-0079-19]

Value	Count	Frequency (%)
of	2073	6.3%
science	1422	4.3%
national	1221	3.7%
foundation	1200	3.6%
and	1114	3.4%
research	899	2.7%
education	825	2.5%
for	666	2.0%
the	661	2.0%
university	418	1.3%
Other values (5172)	22606	68.3%

Most occurring characters

Value	Count	Frequency (%)
	30786	11.4%
n	19062	7.1%
e	16678	6.2%
i	16290	6.0%
a	15502	5.8%
o	15467	5.7%
t	11341	4.2%
r	9949	3.7%
c	9685	3.6%
u	6688	2.5%
Other values (76)	118048	43.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	269496	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	30786	11.4%
n	19062	7.1%
e	16678	6.2%
i	16290	6.0%
a	15502	5.8%
o	15467	5.7%
t	11341	4.2%
r	9949	3.7%
c	9685	3.6%
u	6688	2.5%
Other values (76)	118048	43.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	269496	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	30786	11.4%
n	19062	7.1%
e	16678	6.2%
i	16290	6.0%
a	15502	5.8%
o	15467	5.7%
t	11341	4.2%
r	9949	3.7%
c	9685	3.6%
u	6688	2.5%
Other values (76)	118048	43.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	269496	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	30786	11.4%
n	19062	7.1%
e	16678	6.2%
i	16290	6.0%
a	15502	5.8%
o	15467	5.7%
t	11341	4.2%
r	9949	3.7%
c	9685	3.6%
u	6688	2.5%
Other values (76)	118048	43.8%

Funding Name Preferred
Text

Missing

Distinct	1661
Distinct (%)	72.2%
Missing	2841
Missing (%)	55.3%
Memory size	496.4 KiB

Length

Max length	938
Median length	418
Mean length	131.9409
Min length	1

Characters and Unicode

Total characters	303596
Distinct characters	83
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1509 ?
Unique (%)	65.6%

Sample

1st row	Ministry of Science and Technology of Republic of China(Ministry of Science and Technology, Taiwan)
2nd row	National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC))
3rd row	Second Round of Research Projects for Shanghai Private Colleges
4th row	Hasler Foundation; National Science Foundation(National Science Foundation (NSF)); Direct For Education and Human Resources; Division Of Research On Learning(National Science Foundation (NSF)NSF - Directorate for STEM Education (EDU)); Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)); Div Of Industrial Innovation & Partnersh(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG))
5th row	Institute of Museum and Library Services, USA

Value	Count	Frequency (%)
of	2630	6.9%
science	2311	6.1%
foundation	1474	3.9%
and	1254	3.3%
research	1117	2.9%
	1114	2.9%
education	1074	2.8%
for	1068	2.8%
national	941	2.5%
the	711	1.9%
Other values (3562)	24359	64.0%

Most occurring characters

Value	Count	Frequency (%)
	35755	11.8%
n	25359	8.4%
e	22252	7.3%
i	22112	7.3%
o	21547	7.1%
a	21497	7.1%
t	15642	5.2%
c	13412	4.4%
r	12514	4.1%
u	7963	2.6%
Other values (73)	105543	34.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	303596	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	35755	11.8%
n	25359	8.4%
e	22252	7.3%
i	22112	7.3%
o	21547	7.1%
a	21497	7.1%
t	15642	5.2%
c	13412	4.4%
r	12514	4.1%
u	7963	2.6%
Other values (73)	105543	34.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	303596	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	35755	11.8%
n	25359	8.4%
e	22252	7.3%
i	22112	7.3%
o	21547	7.1%
a	21497	7.1%
t	15642	5.2%
c	13412	4.4%
r	12514	4.1%
u	7963	2.6%
Other values (73)	105543	34.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	303596	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	35755	11.8%
n	25359	8.4%
e	22252	7.3%
i	22112	7.3%
o	21547	7.1%
a	21497	7.1%
t	15642	5.2%
c	13412	4.4%
r	12514	4.1%
u	7963	2.6%
Other values (73)	105543	34.8%

Funding Text
Text

Missing

Distinct	2166
Distinct (%)	93.5%
Missing	2826
Missing (%)	55.0%
Memory size	770.4 KiB

Length

Max length	1613
Median length	564
Mean length	252.52504
Min length	11

Characters and Unicode

Total characters	584848
Distinct characters	91
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2100 ?
Unique (%)	90.7%

Sample

1st row	This research was supported by Ministry of Science and Technology of Republic of China under the number of MOST 106-2511-S-468 -005 -MY2.
2nd row	This work has been finally supported by the National Natural Science Foundation of China (Grant No.61977031) named Research on the Intelligent Comprehensive Assessment of Computational Thinking for the Key Competence.
3rd row	This Research was financially supported by the Second Round of Research Projects for Shanghai Private Colleges (2016-SHNGE-08ZD, Thanks for the help.
4th row	This work is supported by the Hasler Foundation and the National Science Foundation under Grant Numbers 0833612, 1345523, and 0848962. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these foundations.
5th row	This work was supported by the Institute of Museum and Library Services, USA under Grant #LG-14-19-0079-19.

Value	Count	Frequency (%)
the	6760	8.0%
of	3565	4.2%
and	3482	4.1%
this	2424	2.9%
by	2255	2.7%
research	1652	1.9%
for	1412	1.7%
in	1356	1.6%
supported	1320	1.6%
science	1277	1.5%
Other values (9325)	59488	70.0%

Most occurring characters

Value	Count	Frequency (%)
	82685	14.1%
e	44355	7.6%
n	36143	6.2%
a	35041	6.0%
t	34360	5.9%
o	34164	5.8%
i	32012	5.5%
r	29464	5.0%
s	24037	4.1%
h	18673	3.2%
Other values (81)	213914	36.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	584848	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	82685	14.1%
e	44355	7.6%
n	36143	6.2%
a	35041	6.0%
t	34360	5.9%
o	34164	5.8%
i	32012	5.5%
r	29464	5.0%
s	24037	4.1%
h	18673	3.2%
Other values (81)	213914	36.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	584848	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	82685	14.1%
e	44355	7.6%
n	36143	6.2%
a	35041	6.0%
t	34360	5.9%
o	34164	5.8%
i	32012	5.5%
r	29464	5.0%
s	24037	4.1%
h	18673	3.2%
Other values (81)	213914	36.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	584848	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	82685	14.1%
e	44355	7.6%
n	36143	6.2%
a	35041	6.0%
t	34360	5.9%
o	34164	5.8%
i	32012	5.5%
r	29464	5.0%
s	24037	4.1%
h	18673	3.2%
Other values (81)	213914	36.6%

Cited References
Unsupported

Missing Rejected Unsupported

Missing	5142
Missing (%)	100.0%
Memory size	40.3 KiB

Cited Reference Count
Real number (ℝ)

High correlation Zeros

Distinct	182
Distinct (%)	3.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	41.762738

Minimum	0
Maximum	678
Zeros	114
Zeros (%)	2.2%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	17
median	35
Q3	59
95-th percentile	100
Maximum	678
Range	678
Interquartile range (IQR)	42

Descriptive statistics

Standard deviation	33.371033
Coefficient of variation (CV)	0.79906239
Kurtosis	28.081157
Mean	41.762738
Median Absolute Deviation (MAD)	20
Skewness	2.5458066
Sum	214744
Variance	1113.6259
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	114	2.2%
10	92	1.8%
15	89	1.7%
16	85	1.7%
21	85	1.7%
7	84	1.6%
13	82	1.6%
8	82	1.6%
5	82	1.6%
19	81	1.6%
Other values (172)	4266	83.0%

Value	Count	Frequency (%)
0	114	2.2%
1	32	0.6%
2	24	0.5%
3	64	1.2%
4	57	1.1%
5	82	1.6%
6	72	1.4%
7	84	1.6%
8	82	1.6%
9	73	1.4%

Value	Count	Frequency (%)
678	1	< 0.1%
310	1	< 0.1%
262	1	< 0.1%
217	1	< 0.1%
208	2	< 0.1%
207	1	< 0.1%
203	1	< 0.1%
201	1	< 0.1%
191	1	< 0.1%
190	1	< 0.1%

Times Cited, WoS Core
Real number (ℝ)

High correlation Skewed Zeros

Distinct	162
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.083042

Minimum	0
Maximum	3737
Zeros	1472
Zeros (%)	28.6%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	3
Q3	9
95-th percentile	46.95
Maximum	3737
Range	3737
Interquartile range (IQR)	9

Descriptive statistics

Standard deviation	65.510836
Coefficient of variation (CV)	5.4217173
Kurtosis	2088.149
Mean	12.083042
Median Absolute Deviation (MAD)	3
Skewness	39.490117
Sum	62131
Variance	4291.6696
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1472	28.6%
1	656	12.8%
2	434	8.4%
3	318	6.2%
4	229	4.5%
5	211	4.1%
6	173	3.4%
7	159	3.1%
8	111	2.2%
9	109	2.1%
Other values (152)	1270	24.7%

Value	Count	Frequency (%)
0	1472	28.6%
1	656	12.8%
2	434	8.4%
3	318	6.2%
4	229	4.5%
5	211	4.1%
6	173	3.4%
7	159	3.1%
8	111	2.2%
9	109	2.1%

Value	Count	Frequency (%)
3737	1	< 0.1%
1337	1	< 0.1%
969	1	< 0.1%
816	1	< 0.1%
779	1	< 0.1%
752	1	< 0.1%
545	1	< 0.1%
484	1	< 0.1%
455	1	< 0.1%
384	1	< 0.1%

Times Cited, All Databases
Real number (ℝ)

High correlation Skewed Zeros

Distinct	187
Distinct (%)	3.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.186698

Minimum	0
Maximum	4918
Zeros	1307
Zeros (%)	25.4%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	3
Q3	12
95-th percentile	56
Maximum	4918
Range	4918
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	85.736543
Coefficient of variation (CV)	5.6455027
Kurtosis	2136.7135
Mean	15.186698
Median Absolute Deviation (MAD)	3
Skewness	40.13762
Sum	78090
Variance	7350.7549
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1307	25.4%
1	647	12.6%
2	419	8.1%
3	294	5.7%
4	236	4.6%
5	194	3.8%
6	173	3.4%
7	163	3.2%
8	121	2.4%
9	111	2.2%
Other values (177)	1477	28.7%

Value	Count	Frequency (%)
0	1307	25.4%
1	647	12.6%
2	419	8.1%
3	294	5.7%
4	236	4.6%
5	194	3.8%
6	173	3.4%
7	163	3.2%
8	121	2.4%
9	111	2.2%

Value	Count	Frequency (%)
4918	1	< 0.1%
1766	1	< 0.1%
1314	1	< 0.1%
1081	1	< 0.1%
994	1	< 0.1%
918	1	< 0.1%
728	1	< 0.1%
601	1	< 0.1%
545	1	< 0.1%
472	1	< 0.1%

180 Day Usage Count
Real number (ℝ)

High correlation Zeros

Distinct	87
Distinct (%)	1.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.0560093

Minimum	0
Maximum	272
Zeros	2249
Zeros (%)	43.7%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	4
95-th percentile	16
Maximum	272
Range	272
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	11.882995
Coefficient of variation (CV)	2.9297258
Kurtosis	167.75952
Mean	4.0560093
Median Absolute Deviation (MAD)	1
Skewness	10.570871
Sum	20856
Variance	141.20558
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2249	43.7%
1	805	15.7%
2	426	8.3%
3	285	5.5%
4	227	4.4%
5	193	3.8%
6	126	2.5%
7	122	2.4%
8	88	1.7%
9	72	1.4%
Other values (77)	549	10.7%

Value	Count	Frequency (%)
0	2249	43.7%
1	805	15.7%
2	426	8.3%
3	285	5.5%
4	227	4.4%
5	193	3.8%
6	126	2.5%
7	122	2.4%
8	88	1.7%
9	72	1.4%

Value	Count	Frequency (%)
272	1	< 0.1%
259	1	< 0.1%
241	1	< 0.1%
169	1	< 0.1%
168	1	< 0.1%
158	1	< 0.1%
155	1	< 0.1%
145	1	< 0.1%
144	1	< 0.1%
115	2	< 0.1%

Since 2013 Usage Count
Real number (ℝ)

High correlation Zeros

Distinct	283
Distinct (%)	5.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	32.582069

Minimum	0
Maximum	1076
Zeros	430
Zeros (%)	8.4%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	3
median	11
Q3	36
95-th percentile	134
Maximum	1076
Range	1076
Interquartile range (IQR)	33

Descriptive statistics

Standard deviation	64.339985
Coefficient of variation (CV)	1.9747053
Kurtosis	60.15913
Mean	32.582069
Median Absolute Deviation (MAD)	10
Skewness	6.1208886
Sum	167537
Variance	4139.6337
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	430	8.4%
1	339	6.6%
3	316	6.1%
2	310	6.0%
4	251	4.9%
5	231	4.5%
7	184	3.6%
6	148	2.9%
8	132	2.6%
10	118	2.3%
Other values (273)	2683	52.2%

Value	Count	Frequency (%)
0	430	8.4%
1	339	6.6%
2	310	6.0%
3	316	6.1%
4	251	4.9%
5	231	4.5%
6	148	2.9%
7	184	3.6%
8	132	2.6%
9	105	2.0%

Value	Count	Frequency (%)
1076	1	< 0.1%
979	1	< 0.1%
895	1	< 0.1%
853	1	< 0.1%
836	1	< 0.1%
792	1	< 0.1%
729	1	< 0.1%
697	1	< 0.1%
617	1	< 0.1%
613	1	< 0.1%

Publisher
Text

Distinct	345
Distinct (%)	6.7%
Missing	0
Missing (%)	0.0%
Memory size	356.0 KiB

Length

Max length	75
Median length	72
Mean length	21.872423
Min length	4

Characters and Unicode

Total characters	112468
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	173 ?
Unique (%)	3.4%

Sample

1st row	ATLANTIS PRESS
2nd row	SPRINGER INTERNATIONAL PUBLISHING AG
3rd row	ASSOC COMPUTING MACHINERY
4th row	IEEE
5th row	INFORMATION ENGINEERING RESEARCH INST, USA

Value	Count	Frequency (%)
assoc	1064	6.9%
springer	898	5.9%
computing	880	5.7%
machinery	880	5.7%
ieee	729	4.8%
	700	4.6%
ltd	687	4.5%
publishing	371	2.4%
ag	366	2.4%
univ	363	2.4%
Other values (619)	8382	54.7%

Most occurring characters

Value	Count	Frequency (%)
E	11213	10.0%
I	10885	9.7%
	10178	9.0%
N	8859	7.9%
A	7690	6.8%
S	7515	6.7%
R	6903	6.1%
C	6844	6.1%
O	6048	5.4%
T	5769	5.1%
Other values (46)	30564	27.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	112468	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	11213	10.0%
I	10885	9.7%
	10178	9.0%
N	8859	7.9%
A	7690	6.8%
S	7515	6.7%
R	6903	6.1%
C	6844	6.1%
O	6048	5.4%
T	5769	5.1%
Other values (46)	30564	27.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	112468	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	11213	10.0%
I	10885	9.7%
	10178	9.0%
N	8859	7.9%
A	7690	6.8%
S	7515	6.7%
R	6903	6.1%
C	6844	6.1%
O	6048	5.4%
T	5769	5.1%
Other values (46)	30564	27.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	112468	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	11213	10.0%
I	10885	9.7%
	10178	9.0%
N	8859	7.9%
A	7690	6.8%
S	7515	6.7%
R	6903	6.1%
C	6844	6.1%
O	6048	5.4%
T	5769	5.1%
Other values (46)	30564	27.2%

Publisher City
Text

Distinct	236
Distinct (%)	4.6%
Missing	0
Missing (%)	0.0%
Memory size	285.4 KiB

Length

Max length	22
Median length	21
Mean length	7.8160249
Min length	3

Characters and Unicode

Total characters	40190
Distinct characters	57
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	104 ?
Unique (%)	2.0%

Sample

1st row	PARIS
2nd row	CHAM
3rd row	NEW YORK
4th row	NEW YORK
5th row	NEWARK

Value	Count	Frequency (%)
new	1882	24.8%
york	1880	24.7%
cham	365	4.8%
abingdon	334	4.4%
basel	233	3.1%
hoboken	166	2.2%
dordrecht	162	2.1%
oaks	148	1.9%
thousand	148	1.9%
oxford	132	1.7%
Other values (264)	2151	28.3%

Most occurring characters

Value	Count	Frequency (%)
O	4015	10.0%
N	3940	9.8%
E	3594	8.9%
A	3233	8.0%
R	3055	7.6%
	2459	6.1%
K	2286	5.7%
Y	2172	5.4%
W	2025	5.0%
S	1444	3.6%
Other values (47)	11967	29.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	40190	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
O	4015	10.0%
N	3940	9.8%
E	3594	8.9%
A	3233	8.0%
R	3055	7.6%
	2459	6.1%
K	2286	5.7%
Y	2172	5.4%
W	2025	5.0%
S	1444	3.6%
Other values (47)	11967	29.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	40190	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
O	4015	10.0%
N	3940	9.8%
E	3594	8.9%
A	3233	8.0%
R	3055	7.6%
	2459	6.1%
K	2286	5.7%
Y	2172	5.4%
W	2025	5.0%
S	1444	3.6%
Other values (47)	11967	29.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	40190	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
O	4015	10.0%
N	3940	9.8%
E	3594	8.9%
A	3233	8.0%
R	3055	7.6%
	2459	6.1%
K	2286	5.7%
Y	2172	5.4%
W	2025	5.0%
S	1444	3.6%
Other values (47)	11967	29.8%

Publisher Address
Text

Distinct	367
Distinct (%)	7.1%
Missing	0
Missing (%)	0.0%
Memory size	501.0 KiB

Length

Max length	128
Median length	118
Mean length	50.741929
Min length	26

Characters and Unicode

Total characters	260915
Distinct characters	70
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	188 ?
Unique (%)	3.7%

Sample

1st row	29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
2nd row	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
3rd row	1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
4th row	345 E 47TH ST, NEW YORK, NY 10017 USA
5th row	100 CONTINENTAL DR, NEWARK, DE 19713 USA

Value	Count	Frequency (%)
new	2211	5.2%
york	2174	5.1%
ny	1891	4.4%
usa	1705	4.0%
st	1039	2.4%
broadway	859	2.0%
united	849	2.0%
states	842	2.0%
switzerland	721	1.7%
e	717	1.7%
Other values (1648)	29632	69.5%

Most occurring characters

Value	Count	Frequency (%)
	37498	14.4%
A	16293	6.2%
E	15726	6.0%
,	15544	6.0%
N	14738	5.6%
S	11453	4.4%
R	11101	4.3%
T	10257	3.9%
O	9399	3.6%
1	8772	3.4%
Other values (60)	110134	42.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	260915	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	37498	14.4%
A	16293	6.2%
E	15726	6.0%
,	15544	6.0%
N	14738	5.6%
S	11453	4.4%
R	11101	4.3%
T	10257	3.9%
O	9399	3.6%
1	8772	3.4%
Other values (60)	110134	42.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	260915	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	37498	14.4%
A	16293	6.2%
E	15726	6.0%
,	15544	6.0%
N	14738	5.6%
S	11453	4.4%
R	11101	4.3%
T	10257	3.9%
O	9399	3.6%
1	8772	3.4%
Other values (60)	110134	42.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	260915	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	37498	14.4%
A	16293	6.2%
E	15726	6.0%
,	15544	6.0%
N	14738	5.6%
S	11453	4.4%
R	11101	4.3%
T	10257	3.9%
O	9399	3.6%
1	8772	3.4%
Other values (60)	110134	42.2%

ISSN
Text

Missing

Distinct	659
Distinct (%)	19.0%
Missing	1667
Missing (%)	32.4%
Memory size	249.0 KiB

Length

Max length	9
Median length	9
Mean length	9
Min length	9

Characters and Unicode

Total characters	31275
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	357 ?
Unique (%)	10.3%

Sample

1st row	2352-5398
2nd row	2474-0217
3rd row	1943-6092
4th row	0735-6331
5th row	2211-1662

Value	Count	Frequency (%)
1360-2357	186	5.4%
0302-9743	162	4.7%
0190-5848	138	4.0%
0735-6331	107	3.1%
0360-1315	71	2.0%
0899-3408	60	1.7%
2165-9567	60	1.7%
1059-0145	54	1.6%
1049-4820	54	1.6%
1648-5831	52	1.5%
Other values (649)	2531	72.8%

Most occurring characters

Value	Count	Frequency (%)
1	4020	12.9%
0	3811	12.2%
-	3475	11.1%
3	3108	9.9%
2	2937	9.4%
6	2582	8.3%
9	2416	7.7%
5	2315	7.4%
7	2296	7.3%
4	2146	6.9%
Other values (2)	2169	6.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	31275	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	4020	12.9%
0	3811	12.2%
-	3475	11.1%
3	3108	9.9%
2	2937	9.4%
6	2582	8.3%
9	2416	7.7%
5	2315	7.4%
7	2296	7.3%
4	2146	6.9%
Other values (2)	2169	6.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	31275	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	4020	12.9%
0	3811	12.2%
-	3475	11.1%
3	3108	9.9%
2	2937	9.4%
6	2582	8.3%
9	2416	7.7%
5	2315	7.4%
7	2296	7.3%
4	2146	6.9%
Other values (2)	2169	6.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	31275	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	4020	12.9%
0	3811	12.2%
-	3475	11.1%
3	3108	9.9%
2	2937	9.4%
6	2582	8.3%
9	2416	7.7%
5	2315	7.4%
7	2296	7.3%
4	2146	6.9%
Other values (2)	2169	6.9%

eISSN
Text

Missing

Distinct	489
Distinct (%)	19.1%
Missing	2585
Missing (%)	50.3%
Memory size	225.7 KiB

Length

Max length	9
Median length	9
Mean length	9
Min length	9

Characters and Unicode

Total characters	23013
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	279 ?
Unique (%)	10.9%

Sample

1st row	1541-4140
2nd row	2211-1670
3rd row	1573-7608
4th row	1573-7608
5th row	1099-0542

Value	Count	Frequency (%)
1573-7608	186	7.3%
1611-3349	161	6.3%
1541-4140	107	4.2%
2227-7102	85	3.3%
1873-782x	71	2.8%
1744-5175	60	2.3%
1744-5191	54	2.1%
1573-1839	54	2.1%
2335-8971	52	2.0%
1878-0423	51	2.0%
Other values (479)	1676	65.5%

Most occurring characters

Value	Count	Frequency (%)
1	3695	16.1%
-	2557	11.1%
7	2416	10.5%
3	2078	9.0%
5	2077	9.0%
2	2048	8.9%
4	1799	7.8%
0	1646	7.2%
8	1586	6.9%
9	1497	6.5%
Other values (2)	1614	7.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	23013	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	3695	16.1%
-	2557	11.1%
7	2416	10.5%
3	2078	9.0%
5	2077	9.0%
2	2048	8.9%
4	1799	7.8%
0	1646	7.2%
8	1586	6.9%
9	1497	6.5%
Other values (2)	1614	7.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	23013	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	3695	16.1%
-	2557	11.1%
7	2416	10.5%
3	2078	9.0%
5	2077	9.0%
2	2048	8.9%
4	1799	7.8%
0	1646	7.2%
8	1586	6.9%
9	1497	6.5%
Other values (2)	1614	7.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	23013	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	3695	16.1%
-	2557	11.1%
7	2416	10.5%
3	2078	9.0%
5	2077	9.0%
2	2048	8.9%
4	1799	7.8%
0	1646	7.2%
8	1586	6.9%
9	1497	6.5%
Other values (2)	1614	7.0%

ISBN
Text

Missing

Distinct	972
Distinct (%)	40.4%
Missing	2737
Missing (%)	53.2%
Memory size	250.8 KiB

Length

Max length	55
Median length	17
Mean length	21.31185
Min length	13

Characters and Unicode

Total characters	51255
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	611 ?
Unique (%)	25.4%

Sample

1st row	978-94-62520-60-8
2nd row	978-3-319-52691-1; 978-3-319-52690-4
3rd row	978-1-4503-7210-7
4th row	978-1-5386-5059-2
5th row	978-1-16275-049-1

Value	Count	Frequency (%)
978-1-4503-6793-6	44	1.5%
978-988-77034-4-0	32	1.1%
978-1-4503-5103-4	30	1.0%
978-1-4503-8062-1	27	0.9%
978-3-319-52691-1	26	0.9%
978-3-319-52690-4	26	0.9%
978-1-4503-5890-3	25	0.8%
978-1-4503-2605-6	22	0.7%
979-8-3503-6306-7	20	0.7%
979-8-3503-5150-7	20	0.7%
Other values (1270)	2679	90.8%

Most occurring characters

Value	Count	Frequency (%)
-	11803	23.0%
9	5918	11.5%
8	5092	9.9%
7	5087	9.9%
3	4179	8.2%
0	4004	7.8%
1	3960	7.7%
4	3023	5.9%
5	2954	5.8%
6	2298	4.5%
Other values (3)	2937	5.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	51255	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
-	11803	23.0%
9	5918	11.5%
8	5092	9.9%
7	5087	9.9%
3	4179	8.2%
0	4004	7.8%
1	3960	7.7%
4	3023	5.9%
5	2954	5.8%
6	2298	4.5%
Other values (3)	2937	5.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	51255	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
-	11803	23.0%
9	5918	11.5%
8	5092	9.9%
7	5087	9.9%
3	4179	8.2%
0	4004	7.8%
1	3960	7.7%
4	3023	5.9%
5	2954	5.8%
6	2298	4.5%
Other values (3)	2937	5.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	51255	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
-	11803	23.0%
9	5918	11.5%
8	5092	9.9%
7	5087	9.9%
3	4179	8.2%
0	4004	7.8%
1	3960	7.7%
4	3023	5.9%
5	2954	5.8%
6	2298	4.5%
Other values (3)	2937	5.7%

Journal Abbreviation
Text

Missing

Distinct	748
Distinct (%)	19.2%
Missing	1242
Missing (%)	24.2%
Memory size	287.3 KiB

Length

Max length	20
Median length	18
Mean length	16.211795
Min length	3

Characters and Unicode

Total characters	63226
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	403 ?
Unique (%)	10.3%

Sample

1st row	ADV SOC SCI EDUC HUM
2nd row	EDUC COMMUN TECHNOL
3rd row	INT CONF SYST INFORM
4th row	S VIS LANG HUM CEN C
5th row	J EDUC COMPUT RES

Value	Count	Frequency (%)
educ	1591	12.4%
j	750	5.8%
comput	719	5.6%
technol	509	4.0%
sci	436	3.4%
int	401	3.1%
conf	355	2.8%
res	343	2.7%
proc	330	2.6%
inf	280	2.2%
Other values (758)	7115	55.5%

Most occurring characters

Value	Count	Frequency (%)
	8929	14.1%
E	6685	10.6%
C	6312	10.0%
T	4323	6.8%
N	3900	6.2%
O	3767	6.0%
I	3171	5.0%
U	3069	4.9%
S	3008	4.8%
R	2628	4.2%
Other values (43)	17434	27.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	63226	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	8929	14.1%
E	6685	10.6%
C	6312	10.0%
T	4323	6.8%
N	3900	6.2%
O	3767	6.0%
I	3171	5.0%
U	3069	4.9%
S	3008	4.8%
R	2628	4.2%
Other values (43)	17434	27.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	63226	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	8929	14.1%
E	6685	10.6%
C	6312	10.0%
T	4323	6.8%
N	3900	6.2%
O	3767	6.0%
I	3171	5.0%
U	3069	4.9%
S	3008	4.8%
R	2628	4.2%
Other values (43)	17434	27.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	63226	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	8929	14.1%
E	6685	10.6%
C	6312	10.0%
T	4323	6.8%
N	3900	6.2%
O	3767	6.0%
I	3171	5.0%
U	3069	4.9%
S	3008	4.8%
R	2628	4.2%
Other values (43)	17434	27.6%

Journal ISO Abbreviation
Text

Missing

Distinct	586
Distinct (%)	21.7%
Missing	2443
Missing (%)	47.5%
Memory size	255.2 KiB

Length

Max length	53
Median length	37
Mean length	18.80993
Min length	2

Characters and Unicode

Total characters	50768
Distinct characters	64
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	329 ?
Unique (%)	12.2%

Sample

1st row	Educ. Commun. Technol.
2nd row	J. Educ. Comput. Res.
3rd row	Technol. Knowl. Learn.
4th row	Educ. Inf. Technol.
5th row	Educ. Inf. Technol.

Value	Count	Frequency (%)
educ	1401	16.8%
j	744	8.9%
technol	555	6.7%
comput	526	6.3%
sci	401	4.8%
res	329	4.0%
inf	241	2.9%
learn	230	2.8%
int	204	2.5%
trans	97	1.2%
Other values (575)	3593	43.2%

Most occurring characters

Value	Count	Frequency (%)
.	7330	14.4%
	5622	11.1%
c	3233	6.4%
n	2560	5.0%
u	2431	4.8%
e	2392	4.7%
E	2191	4.3%
o	2125	4.2%
t	1916	3.8%
d	1731	3.4%
Other values (54)	19237	37.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	50768	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	7330	14.4%
	5622	11.1%
c	3233	6.4%
n	2560	5.0%
u	2431	4.8%
e	2392	4.7%
E	2191	4.3%
o	2125	4.2%
t	1916	3.8%
d	1731	3.4%
Other values (54)	19237	37.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	50768	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	7330	14.4%
	5622	11.1%
c	3233	6.4%
n	2560	5.0%
u	2431	4.8%
e	2392	4.7%
E	2191	4.3%
o	2125	4.2%
t	1916	3.8%
d	1731	3.4%
Other values (54)	19237	37.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	50768	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	7330	14.4%
	5622	11.1%
c	3233	6.4%
n	2560	5.0%
u	2431	4.8%
e	2392	4.7%
E	2191	4.3%
o	2125	4.2%
t	1916	3.8%
d	1731	3.4%
Other values (54)	19237	37.9%

Publication Date
Text

Missing

Distinct	342
Distinct (%)	14.4%
Missing	2766
Missing (%)	53.8%
Memory size	209.7 KiB

Length

Max length	11
Median length	3
Mean length	4.0841751
Min length	3

Characters and Unicode

Total characters	9704
Distinct characters	31
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	202 ?
Unique (%)	8.5%

Sample

1st row	APR
2nd row	SEP
3rd row	JUL
4th row	AUG
5th row	MAR

Value	Count	Frequency (%)
apr	226	7.2%
dec	219	6.9%
jun	202	6.4%
sep	202	6.4%
jan	202	6.4%
mar	200	6.3%
oct	195	6.2%
jul	192	6.1%
aug	178	5.6%
nov	173	5.5%
Other values (60)	1171	37.1%

Most occurring characters

Value	Count	Frequency (%)
A	1046	10.8%
	784	8.1%
J	657	6.8%
N	628	6.5%
U	617	6.4%
E	603	6.2%
2	470	4.8%
P	465	4.8%
R	462	4.8%
C	441	4.5%
Other values (21)	3531	36.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9704	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
A	1046	10.8%
	784	8.1%
J	657	6.8%
N	628	6.5%
U	617	6.4%
E	603	6.2%
2	470	4.8%
P	465	4.8%
R	462	4.8%
C	441	4.5%
Other values (21)	3531	36.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9704	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
A	1046	10.8%
	784	8.1%
J	657	6.8%
N	628	6.5%
U	617	6.4%
E	603	6.2%
2	470	4.8%
P	465	4.8%
R	462	4.8%
C	441	4.5%
Other values (21)	3531	36.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9704	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
A	1046	10.8%
	784	8.1%
J	657	6.8%
N	628	6.5%
U	617	6.4%
E	603	6.2%
2	470	4.8%
P	465	4.8%
R	462	4.8%
C	441	4.5%
Other values (21)	3531	36.4%

Publication Year
Real number (ℝ)

High correlation

Distinct	25
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2020.7802

Minimum	1994
Maximum	2026
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	1994
5-th percentile	2014
Q1	2019
median	2021
Q3	2024
95-th percentile	2025
Maximum	2026
Range	32
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.5434797
Coefficient of variation (CV)	0.0017535206
Kurtosis	2.4421692
Mean	2020.7802
Median Absolute Deviation (MAD)	2
Skewness	-1.1615206
Sum	10390852
Variance	12.556248
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=25)

Value	Count	Frequency (%)
2025	677	13.2%
2024	632	12.3%
2023	583	11.3%
2022	567	11.0%
2021	565	11.0%
2020	479	9.3%
2019	426	8.3%
2017	315	6.1%
2018	314	6.1%
2016	148	2.9%
Other values (15)	436	8.5%

Value	Count	Frequency (%)
1994	1	< 0.1%
1995	1	< 0.1%
1997	1	< 0.1%
1999	1	< 0.1%
2006	2	< 0.1%
2007	6	0.1%
2008	9	0.2%
2009	17	0.3%
2010	32	0.6%
2011	47	0.9%

Value	Count	Frequency (%)
2026	32	0.6%
2025	677	13.2%
2024	632	12.3%
2023	583	11.3%
2022	567	11.0%
2021	565	11.0%
2020	479	9.3%
2019	426	8.3%
2018	314	6.1%
2017	315	6.1%

Volume
Text

Missing

Distinct	418
Distinct (%)	14.2%
Missing	2192
Missing (%)	42.6%
Memory size	216.3 KiB

Length

Max length	7
Median length	2
Mean length	2.2728814
Min length	1

Characters and Unicode

Total characters	6705
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	218 ?
Unique (%)	7.4%

Sample

1st row	18
2nd row	60
3rd row	28
4th row	27
5th row	28

Value	Count	Frequency (%)
29	106	3.6%
14	104	3.5%
13	92	3.1%
28	83	2.8%
12	79	2.7%
15	73	2.5%
30	70	2.4%
11	70	2.4%
32	64	2.2%
10	60	2.0%
Other values (408)	2149	72.8%

Most occurring characters

Value	Count	Frequency (%)
1	1479	22.1%
2	1100	16.4%
3	800	11.9%
5	590	8.8%
4	552	8.2%
6	499	7.4%
9	464	6.9%
0	450	6.7%
8	393	5.9%
7	373	5.6%
Other values (2)	5	0.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6705	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1479	22.1%
2	1100	16.4%
3	800	11.9%
5	590	8.8%
4	552	8.2%
6	499	7.4%
9	464	6.9%
0	450	6.7%
8	393	5.9%
7	373	5.6%
Other values (2)	5	0.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6705	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1479	22.1%
2	1100	16.4%
3	800	11.9%
5	590	8.8%
4	552	8.2%
6	499	7.4%
9	464	6.9%
0	450	6.7%
8	393	5.9%
7	373	5.6%
Other values (2)	5	0.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6705	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1479	22.1%
2	1100	16.4%
3	800	11.9%
5	590	8.8%
4	552	8.2%
6	499	7.4%
9	464	6.9%
0	450	6.7%
8	393	5.9%
7	373	5.6%
Other values (2)	5	0.1%

Issue
Text

Missing

Distinct	92
Distinct (%)	4.4%
Missing	3041
Missing (%)	59.1%
Memory size	198.1 KiB

Length

Max length	7
Median length	1
Mean length	1.1761066
Min length	1

Characters and Unicode

Total characters	2471
Distinct characters	22
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	44 ?
Unique (%)	2.1%

Sample

1st row	2
2nd row	3
3rd row	6
4th row	8
5th row	2

Value	Count	Frequency (%)
1	429	20.4%
2	341	16.2%
3	304	14.5%
4	258	12.3%
6	145	6.9%
5	129	6.1%
7	76	3.6%
8	52	2.5%
9	50	2.4%
10	38	1.8%
Other values (82)	281	13.4%

Most occurring characters

Value	Count	Frequency (%)
1	656	26.5%
2	430	17.4%
3	363	14.7%
4	301	12.2%
6	188	7.6%
5	157	6.4%
7	109	4.4%
8	92	3.7%
0	71	2.9%
9	66	2.7%
Other values (12)	38	1.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2471	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	656	26.5%
2	430	17.4%
3	363	14.7%
4	301	12.2%
6	188	7.6%
5	157	6.4%
7	109	4.4%
8	92	3.7%
0	71	2.9%
9	66	2.7%
Other values (12)	38	1.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2471	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	656	26.5%
2	430	17.4%
3	363	14.7%
4	301	12.2%
6	188	7.6%
5	157	6.4%
7	109	4.4%
8	92	3.7%
0	71	2.9%
9	66	2.7%
Other values (12)	38	1.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2471	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	656	26.5%
2	430	17.4%
3	363	14.7%
4	301	12.2%
6	188	7.6%
5	157	6.4%
7	109	4.4%
8	92	3.7%
0	71	2.9%
9	66	2.7%
Other values (12)	38	1.5%

Part Number
Text

Missing

Distinct	6
Distinct (%)	66.7%
Missing	5133
Missing (%)	99.8%
Memory size	161.0 KiB

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	9
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	33.3%

Sample

1st row	A
2nd row	2
3rd row	E
4th row	1
5th row	B

Value	Count	Frequency (%)
2	2	22.2%
1	2	22.2%
b	2	22.2%
a	1	11.1%
e	1	11.1%
c	1	11.1%

Most occurring characters

Value	Count	Frequency (%)
2	2	22.2%
1	2	22.2%
B	2	22.2%
A	1	11.1%
E	1	11.1%
C	1	11.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	2	22.2%
1	2	22.2%
B	2	22.2%
A	1	11.1%
E	1	11.1%
C	1	11.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	2	22.2%
1	2	22.2%
B	2	22.2%
A	1	11.1%
E	1	11.1%
C	1	11.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	2	22.2%
1	2	22.2%
B	2	22.2%
A	1	11.1%
E	1	11.1%
C	1	11.1%

Supplement
Categorical

High correlation Imbalance Missing

Distinct	4
Distinct (%)	23.5%
Missing	5125
Missing (%)	99.7%
Memory size	281.2 KiB

1	14
3	1
S1	1
S	1

Length

Max length	2
Median length	1
Mean length	1.0588235
Min length	1

Characters and Unicode

Total characters	18
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	17.6%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	3

Common Values

Value	Count	Frequency (%)
1	14	0.3%
3	1	< 0.1%
S1	1	< 0.1%
S	1	< 0.1%
(Missing)	5125	99.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1	14	82.4%
3	1	5.9%
s1	1	5.9%
s	1	5.9%

Most occurring characters

Value	Count	Frequency (%)
1	15	83.3%
S	2	11.1%
3	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	15	83.3%
S	2	11.1%
3	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	15	83.3%
S	2	11.1%
3	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	15	83.3%
S	2	11.1%
3	1	5.6%

Special Issue
Categorical

High correlation Imbalance Missing

Distinct	2
Distinct (%)	1.0%
Missing	4937
Missing (%)	96.0%
Memory size	280.3 KiB

SI	203
2	2

Length

Max length	2
Median length	2
Mean length	1.9902439
Min length	1

Characters and Unicode

Total characters	408
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	SI
2nd row	SI
3rd row	SI
4th row	SI
5th row	SI

Common Values

Value	Count	Frequency (%)
SI	203	3.9%
2	2	< 0.1%
(Missing)	4937	96.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
si	203	99.0%
2	2	1.0%

Most occurring characters

Value	Count	Frequency (%)
S	203	49.8%
I	203	49.8%
2	2	0.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	408	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	203	49.8%
I	203	49.8%
2	2	0.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	408	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	203	49.8%
I	203	49.8%
2	2	0.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	408	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	203	49.8%
I	203	49.8%
2	2	0.5%

Meeting Abstract
Text

Missing

Distinct	4
Distinct (%)	100.0%
Missing	5138
Missing (%)	99.9%
Memory size	160.9 KiB

Length

Max length	9
Median length	6.5
Mean length	5
Min length	3

Characters and Unicode

Total characters	20
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	100.0%

Sample

1st row	145
2nd row	2078
3rd row	LA18
4th row	LHMM23011

Value	Count	Frequency (%)
145	1	25.0%
2078	1	25.0%
la18	1	25.0%
lhmm23011	1	25.0%

Most occurring characters

Value	Count	Frequency (%)
1	4	20.0%
2	2	10.0%
8	2	10.0%
0	2	10.0%
L	2	10.0%
M	2	10.0%
5	1	5.0%
4	1	5.0%
7	1	5.0%
A	1	5.0%
Other values (2)	2	10.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	20	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	4	20.0%
2	2	10.0%
8	2	10.0%
0	2	10.0%
L	2	10.0%
M	2	10.0%
5	1	5.0%
4	1	5.0%
7	1	5.0%
A	1	5.0%
Other values (2)	2	10.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	20	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	4	20.0%
2	2	10.0%
8	2	10.0%
0	2	10.0%
L	2	10.0%
M	2	10.0%
5	1	5.0%
4	1	5.0%
7	1	5.0%
A	1	5.0%
Other values (2)	2	10.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	20	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	4	20.0%
2	2	10.0%
8	2	10.0%
0	2	10.0%
L	2	10.0%
M	2	10.0%
5	1	5.0%
4	1	5.0%
7	1	5.0%
A	1	5.0%
Other values (2)	2	10.0%

Start Page
Text

Missing

Distinct	1568
Distinct (%)	42.4%
Missing	1440
Missing (%)	28.0%
Memory size	233.2 KiB

Length

Max length	6
Median length	3
Mean length	3.0229606
Min length	1

Characters and Unicode

Total characters	11191
Distinct characters	16
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	893 ?
Unique (%)	24.1%

Sample

1st row	817
2nd row	291
3rd row	364
4th row	129
5th row	376

Value	Count	Frequency (%)
1	47	1.3%
3	25	0.7%
55	15	0.4%
213	14	0.4%
57	14	0.4%
85	14	0.4%
133	13	0.4%
65	12	0.3%
33	12	0.3%
15	12	0.3%
Other values (1558)	3524	95.2%

Most occurring characters

Value	Count	Frequency (%)
1	1941	17.3%
3	1366	12.2%
2	1263	11.3%
5	1183	10.6%
4	1018	9.1%
7	1004	9.0%
9	951	8.5%
6	916	8.2%
8	796	7.1%
0	721	6.4%
Other values (6)	32	0.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11191	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1941	17.3%
3	1366	12.2%
2	1263	11.3%
5	1183	10.6%
4	1018	9.1%
7	1004	9.0%
9	951	8.5%
6	916	8.2%
8	796	7.1%
0	721	6.4%
Other values (6)	32	0.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11191	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1941	17.3%
3	1366	12.2%
2	1263	11.3%
5	1183	10.6%
4	1018	9.1%
7	1004	9.0%
9	951	8.5%
6	916	8.2%
8	796	7.1%
0	721	6.4%
Other values (6)	32	0.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11191	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1941	17.3%
3	1366	12.2%
2	1263	11.3%
5	1183	10.6%
4	1018	9.1%
7	1004	9.0%
9	951	8.5%
6	916	8.2%
8	796	7.1%
0	721	6.4%
Other values (6)	32	0.3%

End Page
Text

Missing

Distinct	1586
Distinct (%)	42.8%
Missing	1440
Missing (%)	28.0%
Memory size	233.3 KiB

Length

Max length	6
Median length	3
Mean length	3.0599676
Min length	1

Characters and Unicode

Total characters	11328
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	886 ?
Unique (%)	23.9%

Sample

1st row	821
2nd row	305
3rd row	368
4th row	131
5th row	380

Value	Count	Frequency (%)
	16	0.4%
28	13	0.4%
80	12	0.3%
57	12	0.3%
138	12	0.3%
17	12	0.3%
112	12	0.3%
62	12	0.3%
41	11	0.3%
24	11	0.3%
Other values (1576)	3579	96.7%

Most occurring characters

Value	Count	Frequency (%)
1	1874	16.5%
2	1434	12.7%
3	1203	10.6%
4	1130	10.0%
5	1046	9.2%
6	1015	9.0%
8	958	8.5%
7	906	8.0%
0	875	7.7%
9	850	7.5%
Other values (7)	37	0.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11328	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1874	16.5%
2	1434	12.7%
3	1203	10.6%
4	1130	10.0%
5	1046	9.2%
6	1015	9.0%
8	958	8.5%
7	906	8.0%
0	875	7.7%
9	850	7.5%
Other values (7)	37	0.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11328	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1874	16.5%
2	1434	12.7%
3	1203	10.6%
4	1130	10.0%
5	1046	9.2%
6	1015	9.0%
8	958	8.5%
7	906	8.0%
0	875	7.7%
9	850	7.5%
Other values (7)	37	0.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11328	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1874	16.5%
2	1434	12.7%
3	1203	10.6%
4	1130	10.0%
5	1046	9.2%
6	1015	9.0%
8	958	8.5%
7	906	8.0%
0	875	7.7%
9	850	7.5%
Other values (7)	37	0.3%

Article Number
Text

Missing

Distinct	796
Distinct (%)	82.3%
Missing	4175
Missing (%)	81.2%
Memory size	181.7 KiB

Length

Max length	18
Median length	17
Mean length	5.131334
Min length	1

Characters and Unicode

Total characters	4962
Distinct characters	30
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	732 ?
Unique (%)	75.7%

Sample

1st row	7356331211033158
2nd row	15
3rd row	735633120965919
4th row	20133
5th row	735633120988807

Value	Count	Frequency (%)
19	10	1.0%
1	9	0.9%
5	8	0.8%
6	8	0.8%
12	8	0.8%
11	8	0.8%
10	7	0.7%
7	7	0.7%
27	6	0.6%
4	6	0.6%
Other values (786)	890	92.0%

Most occurring characters

Value	Count	Frequency (%)
1	961	19.4%
0	727	14.7%
2	523	10.5%
3	486	9.8%
5	413	8.3%
4	400	8.1%
7	372	7.5%
6	335	6.8%
9	324	6.5%
8	307	6.2%
Other values (20)	114	2.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4962	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	961	19.4%
0	727	14.7%
2	523	10.5%
3	486	9.8%
5	413	8.3%
4	400	8.1%
7	372	7.5%
6	335	6.8%
9	324	6.5%
8	307	6.2%
Other values (20)	114	2.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4962	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	961	19.4%
0	727	14.7%
2	523	10.5%
3	486	9.8%
5	413	8.3%
4	400	8.1%
7	372	7.5%
6	335	6.8%
9	324	6.5%
8	307	6.2%
Other values (20)	114	2.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4962	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	961	19.4%
0	727	14.7%
2	523	10.5%
3	486	9.8%
5	413	8.3%
4	400	8.1%
7	372	7.5%
6	335	6.8%
9	324	6.5%
8	307	6.2%
Other values (20)	114	2.3%

DOI
Text

Missing

Distinct	4191
Distinct (%)	> 99.9%
Missing	950
Missing (%)	18.5%
Memory size	333.0 KiB

Length

Max length	55
Median length	43
Mean length	25.056536
Min length	13

Characters and Unicode

Total characters	105037
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4190 ?
Unique (%)	> 99.9%

Sample

1st row	10.1007/978-3-319-52691-1_18
2nd row	10.1145/3345120.3345134
3rd row	10.1109/IC3.2018.00-45
4th row	10.1145/3629296.3629361
5th row	10.1145/3649217.3653565

Value	Count	Frequency (%)
10.1145/3408877.3439639	2	< 0.1%
10.1080/03057267.2021.1963580	1	< 0.1%
10.1145/3514262.3514311	1	< 0.1%
10.1109/weit.2013.27	1	< 0.1%
10.1007/978-3-031-38454-7_12	1	< 0.1%
10.1109/icetc.2009.16	1	< 0.1%
10.4028/www.scientific.net/amm.373-375.2200	1	< 0.1%
10.1002/cae.22669	1	< 0.1%
10.1145/3345120.3345134	1	< 0.1%
10.1007/s10639-022-11454-1	1	< 0.1%
Other values (4181)	4181	99.7%

Most occurring characters

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (58)	22448	21.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (58)	22448	21.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (58)	22448	21.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (58)	22448	21.4%

DOI Link
Categorical

Constant Missing

Distinct	1
Distinct (%)	< 0.1%
Missing	950
Missing (%)	18.5%
Memory size	265.0 KiB

0.0	4192

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	12576
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	4192	81.5%
(Missing)	950	18.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0.0	4192	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	8384	66.7%
.	4192	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12576	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	8384	66.7%
.	4192	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12576	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	8384	66.7%
.	4192	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12576	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	8384	66.7%
.	4192	33.3%

Book DOI
Categorical

High correlation Missing

Distinct	31
Distinct (%)	42.5%
Missing	5069
Missing (%)	98.6%
Memory size	282.6 KiB

10.1007/978-3-319-52691-1	25
10.1007/978-3-319-71054-9	6
10.4102/aosis.2023.BK409	4
10.7551/mitpress/11209.001.0001	3
10.1017/ 9781108654555	3
Other values (26)	32

Length

Max length	31
Median length	25
Mean length	24.780822
Min length	21

Characters and Unicode

Total characters	1809
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	21 ?
Unique (%)	28.8%

Sample

1st row	10.1007/978-3-319-52691-1
2nd row	10.1007/978-3-319-52691-1
3rd row	10.1007/978-3-319-52691-1
4th row	10.1007/978-3-319-52691-1
5th row	10.1007/978-3-319-52691-1

Common Values

Value	Count	Frequency (%)
10.1007/978-3-319-52691-1	25	0.5%
10.1007/978-3-319-71054-9	6	0.1%
10.4102/aosis.2023.BK409	4	0.1%
10.7551/mitpress/11209.001.0001	3	0.1%
10.1017/ 9781108654555	3	0.1%
10.1007/978-3-319-66020-2	3	0.1%
10.4018/978-1-4666-1864-0	2	< 0.1%
10.4018/978-1-5225-2255-3	2	< 0.1%
10.4018/978-1-4666-0182-6	2	< 0.1%
10.4324/9780429319501	2	< 0.1%
Other values (21)	21	0.4%
(Missing)	5069	98.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
10.1007/978-3-319-52691-1	25	32.9%
10.1007/978-3-319-71054-9	6	7.9%
10.4102/aosis.2023.bk409	4	5.3%
10.7551/mitpress/11209.001.0001	3	3.9%
10.1017	3	3.9%
9781108654555	3	3.9%
10.1007/978-3-319-66020-2	3	3.9%
10.4018/978-1-4666-1864-0	2	2.6%
10.4018/978-1-5225-2255-3	2	2.6%
10.4018/978-1-4666-0182-6	2	2.6%
Other values (22)	23	30.3%

Most occurring characters

Value	Count	Frequency (%)
1	310	17.1%
0	261	14.4%
-	220	12.2%
9	162	9.0%
7	134	7.4%
3	107	5.9%
8	97	5.4%
.	89	4.9%
6	78	4.3%
/	77	4.3%
Other values (15)	274	15.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1809	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	310	17.1%
0	261	14.4%
-	220	12.2%
9	162	9.0%
7	134	7.4%
3	107	5.9%
8	97	5.4%
.	89	4.9%
6	78	4.3%
/	77	4.3%
Other values (15)	274	15.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1809	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	310	17.1%
0	261	14.4%
-	220	12.2%
9	162	9.0%
7	134	7.4%
3	107	5.9%
8	97	5.4%
.	89	4.9%
6	78	4.3%
/	77	4.3%
Other values (15)	274	15.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1809	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	310	17.1%
0	261	14.4%
-	220	12.2%
9	162	9.0%
7	134	7.4%
3	107	5.9%
8	97	5.4%
.	89	4.9%
6	78	4.3%
/	77	4.3%
Other values (15)	274	15.1%

Early Access Date
Date

Missing

Distinct	84
Distinct (%)	6.9%
Missing	3926
Missing (%)	76.4%
Memory size	40.3 KiB

Minimum	2019-03-01 00:00:00
Maximum	2026-03-01 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=50)

Number of Pages
Real number (ℝ)

High correlation

Distinct	66
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.878063

Minimum	1
Maximum	433
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	6
median	12
Q3	19
95-th percentile	31
Maximum	433
Range	432
Interquartile range (IQR)	13

Descriptive statistics

Standard deviation	13.908785
Coefficient of variation (CV)	1.0022137
Kurtosis	361.85436
Mean	13.878063
Median Absolute Deviation (MAD)	6
Skewness	14.002221
Sum	71361
Variance	193.4543
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
6	384	7.5%
7	330	6.4%
5	273	5.3%
8	258	5.0%
4	235	4.6%
12	232	4.5%
10	222	4.3%
9	217	4.2%
16	196	3.8%
2	189	3.7%
Other values (56)	2606	50.7%

Value	Count	Frequency (%)
1	156	3.0%
2	189	3.7%
3	110	2.1%
4	235	4.6%
5	273	5.3%
6	384	7.5%
7	330	6.4%
8	258	5.0%
9	217	4.2%
10	222	4.3%

Value	Count	Frequency (%)
433	1	< 0.1%
423	1	< 0.1%
278	1	< 0.1%
266	1	< 0.1%
192	1	< 0.1%
185	1	< 0.1%
144	1	< 0.1%
80	1	< 0.1%
76	1	< 0.1%
66	1	< 0.1%

WoS Categories
Text

Distinct	473
Distinct (%)	9.2%
Missing	15
Missing (%)	0.3%
Memory size	560.0 KiB

Length

Max length	258
Median length	187
Mean length	62.722255
Min length	3

Characters and Unicode

Total characters	321577
Distinct characters	48
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	243 ?
Unique (%)	4.7%

Sample

1st row	Education & Educational Research; Social Sciences, Interdisciplinary
2nd row	Education & Educational Research; Social Issues
3rd row	Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods
4th row	Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic
5th row	Economics; Education & Educational Research; Management

Value	Count	Frequency (%)
education	4149	12.4%
	4144	12.4%
science	3348	10.0%
computer	3174	9.5%
educational	2673	8.0%
research	2632	7.8%
scientific	1512	4.5%
disciplines	1512	4.5%
interdisciplinary	1179	3.5%
applications	1086	3.2%
Other values (157)	8134	24.2%

Most occurring characters

Value	Count	Frequency (%)
i	33260	10.3%
	28416	8.8%
e	27741	8.6%
c	27355	8.5%
n	23457	7.3%
t	18983	5.9%
a	17569	5.5%
o	16158	5.0%
r	13305	4.1%
s	11632	3.6%
Other values (38)	103701	32.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	321577	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	33260	10.3%
	28416	8.8%
e	27741	8.6%
c	27355	8.5%
n	23457	7.3%
t	18983	5.9%
a	17569	5.5%
o	16158	5.0%
r	13305	4.1%
s	11632	3.6%
Other values (38)	103701	32.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	321577	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	33260	10.3%
	28416	8.8%
e	27741	8.6%
c	27355	8.5%
n	23457	7.3%
t	18983	5.9%
a	17569	5.5%
o	16158	5.0%
r	13305	4.1%
s	11632	3.6%
Other values (38)	103701	32.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	321577	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	33260	10.3%
	28416	8.8%
e	27741	8.6%
c	27355	8.5%
n	23457	7.3%
t	18983	5.9%
a	17569	5.5%
o	16158	5.0%
r	13305	4.1%
s	11632	3.6%
Other values (38)	103701	32.2%

Web of Science Index
Categorical

High correlation

Distinct	19
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	577.6 KiB

Conference Proceedings Citation Index - Science (CPCI-S)	1439
Social Science Citation Index (SSCI)	991
Emerging Sources Citation Index (ESCI)	961
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	703
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)	355
Other values (14)	693

Length

Max length	185
Median length	136
Mean length	62.674057
Min length	36

Characters and Unicode

Total characters	322270
Distinct characters	37
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	0.1%

Sample

1st row	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
2nd row	Book Citation Index– Social Sciences & Humanities (BKCI-SSH)
3rd row	Conference Proceedings Citation Index - Science (CPCI-S)
4th row	Conference Proceedings Citation Index - Science (CPCI-S)
5th row	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)

Common Values

Value	Count	Frequency (%)
Conference Proceedings Citation Index - Science (CPCI-S)	1439	28.0%
Social Science Citation Index (SSCI)	991	19.3%
Emerging Sources Citation Index (ESCI)	961	18.7%
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	703	13.7%
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)	355	6.9%
Science Citation Index Expanded (SCI-EXPANDED)	289	5.6%
Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	231	4.5%
Book Citation Index– Social Sciences & Humanities (BKCI-SSH)	56	1.1%
Book Citation Index– Social Sciences & Humanities (BKCI-SSH); Book Citation Index– Science (BKCI-S)	35	0.7%
Arts & Humanities Citation Index (A&HCI)	25	0.5%
Other values (9)	57	1.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
citation	6292	15.4%
index	6152	15.1%
science	5182	12.7%
	4174	10.2%
conference	3089	7.6%
proceedings	3089	7.6%
social	2405	5.9%
cpci-s	2153	5.3%
ssci	1377	3.4%
humanities	1085	2.7%
Other values (13)	5781	14.2%

Most occurring characters

Value	Count	Frequency (%)
e	35960	11.2%
	35637	11.1%
n	29839	9.3%
i	26483	8.2%
c	20092	6.2%
C	18762	5.8%
S	17280	5.4%
o	16116	5.0%
t	13726	4.3%
I	12584	3.9%
Other values (27)	95791	29.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	322270	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	35960	11.2%
	35637	11.1%
n	29839	9.3%
i	26483	8.2%
c	20092	6.2%
C	18762	5.8%
S	17280	5.4%
o	16116	5.0%
t	13726	4.3%
I	12584	3.9%
Other values (27)	95791	29.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	322270	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	35960	11.2%
	35637	11.1%
n	29839	9.3%
i	26483	8.2%
c	20092	6.2%
C	18762	5.8%
S	17280	5.4%
o	16116	5.0%
t	13726	4.3%
I	12584	3.9%
Other values (27)	95791	29.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	322270	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	35960	11.2%
	35637	11.1%
n	29839	9.3%
i	26483	8.2%
c	20092	6.2%
C	18762	5.8%
S	17280	5.4%
o	16116	5.0%
t	13726	4.3%
I	12584	3.9%
Other values (27)	95791	29.7%

Research Areas
Text

Distinct	227
Distinct (%)	4.4%
Missing	15
Missing (%)	0.3%
Memory size	427.1 KiB

Length

Max length	140
Median length	139
Mean length	36.180417
Min length	3

Characters and Unicode

Total characters	185497
Distinct characters	47
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	134 ?
Unique (%)	2.6%

Sample

1st row	Education & Educational Research; Social Sciences - Other Topics
2nd row	Education & Educational Research; Social Issues
3rd row	Computer Science
4th row	Computer Science; Engineering
5th row	Business & Economics; Education & Educational Research

Value	Count	Frequency (%)
	4196	18.7%
research	3606	16.0%
education	3599	16.0%
educational	3599	16.0%
science	2342	10.4%
computer	2131	9.5%
engineering	666	3.0%
other	219	1.0%
topics	219	1.0%
psychology	195	0.9%
Other values (120)	1721	7.7%

Most occurring characters

Value	Count	Frequency (%)
	17366	9.4%
c	17252	9.3%
e	16869	9.1%
a	15249	8.2%
n	12667	6.8%
i	12594	6.8%
o	11526	6.2%
t	10503	5.7%
u	9780	5.3%
E	8025	4.3%
Other values (37)	53666	28.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	185497	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	17366	9.4%
c	17252	9.3%
e	16869	9.1%
a	15249	8.2%
n	12667	6.8%
i	12594	6.8%
o	11526	6.2%
t	10503	5.7%
u	9780	5.3%
E	8025	4.3%
Other values (37)	53666	28.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	185497	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	17366	9.4%
c	17252	9.3%
e	16869	9.1%
a	15249	8.2%
n	12667	6.8%
i	12594	6.8%
o	11526	6.2%
t	10503	5.7%
u	9780	5.3%
E	8025	4.3%
Other values (37)	53666	28.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	185497	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	17366	9.4%
c	17252	9.3%
e	16869	9.1%
a	15249	8.2%
n	12667	6.8%
i	12594	6.8%
o	11526	6.2%
t	10503	5.7%
u	9780	5.3%
E	8025	4.3%
Other values (37)	53666	28.9%

IDS Number
Text

Distinct	3146
Distinct (%)	61.2%
Missing	0
Missing (%)	0.0%
Memory size	271.3 KiB

Length

Max length	5
Median length	5
Mean length	5
Min length	5

Characters and Unicode

Total characters	25710
Distinct characters	36
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2482 ?
Unique (%)	48.3%

Sample

1st row	BD3LM
2nd row	BJ2PE
3rd row	BO5OP
4th row	BM3GW
5th row	BGC87

Value	Count	Frequency (%)
bt2kw	44	0.9%
bp1xa	32	0.6%
bn4hw	30	0.6%
by3po	27	0.5%
bj2pe	26	0.5%
bq1ej	25	0.5%
bo0zi	22	0.4%
by4or	20	0.4%
bt3ph	19	0.4%
bm7tq	17	0.3%
Other values (3136)	4880	94.9%

Most occurring characters

Value	Count	Frequency (%)
B	2926	11.4%
P	809	3.1%
1	807	3.1%
4	778	3.0%
W	759	3.0%
3	750	2.9%
2	749	2.9%
6	746	2.9%
0	723	2.8%
O	721	2.8%
Other values (26)	15942	62.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	25710	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
B	2926	11.4%
P	809	3.1%
1	807	3.1%
4	778	3.0%
W	759	3.0%
3	750	2.9%
2	749	2.9%
6	746	2.9%
0	723	2.8%
O	721	2.8%
Other values (26)	15942	62.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	25710	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
B	2926	11.4%
P	809	3.1%
1	807	3.1%
4	778	3.0%
W	759	3.0%
3	750	2.9%
2	749	2.9%
6	746	2.9%
0	723	2.8%
O	721	2.8%
Other values (26)	15942	62.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	25710	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
B	2926	11.4%
P	809	3.1%
1	807	3.1%
4	778	3.0%
W	759	3.0%
3	750	2.9%
2	749	2.9%
6	746	2.9%
0	723	2.8%
O	721	2.8%
Other values (26)	15942	62.0%

Pubmed Id
Real number (ℝ)

High correlation Missing

Distinct	163
Distinct (%)	100.0%
Missing	4979
Missing (%)	96.8%
Infinite	0
Infinite (%)	0.0%
Mean	35298568

Minimum	7724567
Maximum	41315544
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	40.3 KiB

Quantile statistics

Minimum	7724567
5-th percentile	25411797
Q1	34480024
median	35769741
Q3	37663861
95-th percentile	40809543
Maximum	41315544
Range	33590977
Interquartile range (IQR)	3183836.5

Descriptive statistics

Standard deviation	4664770.2
Coefficient of variation (CV)	0.13215183
Kurtosis	8.5704868
Mean	35298568
Median Absolute Deviation (MAD)	1592030
Skewness	-2.3141949
Sum	5.7536665 × 10⁹
Variance	2.1760081 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
41094117	1	< 0.1%
36467441	1	< 0.1%
34824997	1	< 0.1%
38921690	1	< 0.1%
34566735	1	< 0.1%
39035481	1	< 0.1%
35632157	1	< 0.1%
36582327	1	< 0.1%
35627539	1	< 0.1%
18672462	1	< 0.1%
Other values (153)	153	3.0%
(Missing)	4979	96.8%

Value	Count	Frequency (%)
7724567	1	< 0.1%
18672462	1	< 0.1%
21563975	1	< 0.1%
22068329	1	< 0.1%
23194371	1	< 0.1%
23385287	1	< 0.1%
23449003	1	< 0.1%
24719575	1	< 0.1%
25411792	1	< 0.1%
25411839	1	< 0.1%

Value	Count	Frequency (%)
41315544	1	< 0.1%
41305265	1	< 0.1%
41295426	1	< 0.1%
41094117	1	< 0.1%
41006537	1	< 0.1%
41000524	1	< 0.1%
40901815	1	< 0.1%
40897146	1	< 0.1%
40820512	1	< 0.1%
40710821	1	< 0.1%

Open Access Designations
Categorical

High correlation Missing

Distinct	28
Distinct (%)	1.7%
Missing	3457
Missing (%)	67.2%
Memory size	288.6 KiB

gold	665
Green Submitted, gold	409
hybrid	240
Green Submitted	105
Green Submitted, hybrid	80
Other values (23)	186

Length

Max length	40
Median length	39
Mean length	11.400593
Min length	4

Characters and Unicode

Total characters	19210
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9 ?
Unique (%)	0.5%

Sample

1st row	Green Submitted, hybrid
2nd row	hybrid
3rd row	hybrid
4th row	hybrid
5th row	Green Accepted

Common Values

Value	Count	Frequency (%)
gold	665	12.9%
Green Submitted, gold	409	8.0%
hybrid	240	4.7%
Green Submitted	105	2.0%
Green Submitted, hybrid	80	1.6%
Bronze	72	1.4%
Green Submitted, Bronze	36	0.7%
Green Published	16	0.3%
Green Published, hybrid	8	0.2%
Green Published, gold	7	0.1%
Other values (18)	47	0.9%
(Missing)	3457	67.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
gold	1098	36.5%
green	728	24.2%
submitted	649	21.6%
hybrid	336	11.2%
bronze	120	4.0%
published	47	1.6%
accepted	32	1.1%

Most occurring characters

Value	Count	Frequency (%)
e	2336	12.2%
d	2162	11.3%
t	1330	6.9%
	1325	6.9%
o	1218	6.3%
r	1184	6.2%
l	1145	6.0%
g	1098	5.7%
b	1032	5.4%
i	1032	5.4%
Other values (15)	5348	27.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	19210	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	2336	12.2%
d	2162	11.3%
t	1330	6.9%
	1325	6.9%
o	1218	6.3%
r	1184	6.2%
l	1145	6.0%
g	1098	5.7%
b	1032	5.4%
i	1032	5.4%
Other values (15)	5348	27.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	19210	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	2336	12.2%
d	2162	11.3%
t	1330	6.9%
	1325	6.9%
o	1218	6.3%
r	1184	6.2%
l	1145	6.0%
g	1098	5.7%
b	1032	5.4%
i	1032	5.4%
Other values (15)	5348	27.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	19210	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	2336	12.2%
d	2162	11.3%
t	1330	6.9%
	1325	6.9%
o	1218	6.3%
r	1184	6.2%
l	1145	6.0%
g	1098	5.7%
b	1032	5.4%
i	1032	5.4%
Other values (15)	5348	27.8%

Highly Cited Status
Boolean

Constant Missing

Distinct	1
Distinct (%)	7.1%
Missing	5128
Missing (%)	99.7%
Memory size	10.2 KiB

True	14
(Missing)	5128

Value	Count	Frequency (%)
True	14	0.3%
(Missing)	5128	99.7%

Hot Paper Status
Boolean

Constant Missing

Distinct	1
Distinct (%)	7.1%
Missing	5128
Missing (%)	99.7%
Memory size	10.2 KiB

False	14
(Missing)	5128

Value	Count	Frequency (%)
False	14	0.3%
(Missing)	5128	99.7%

Date of Export
Categorical

Constant

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	296.4 KiB

2025-12-30	5142

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	51420
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2025-12-30
2nd row	2025-12-30
3rd row	2025-12-30
4th row	2025-12-30
5th row	2025-12-30

Common Values

Value	Count	Frequency (%)
2025-12-30	5142	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2025-12-30	5142	100.0%

Most occurring characters

Value	Count	Frequency (%)
2	15426	30.0%
0	10284	20.0%
-	10284	20.0%
5	5142	10.0%
1	5142	10.0%
3	5142	10.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	51420	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	15426	30.0%
0	10284	20.0%
-	10284	20.0%
5	5142	10.0%
1	5142	10.0%
3	5142	10.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	51420	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	15426	30.0%
0	10284	20.0%
-	10284	20.0%
5	5142	10.0%
1	5142	10.0%
3	5142	10.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	51420	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	15426	30.0%
0	10284	20.0%
-	10284	20.0%
5	5142	10.0%
1	5142	10.0%
3	5142	10.0%

UT (Unique WOS ID)
Text

Unique

Distinct	5142
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	341.6 KiB

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	97698
Distinct characters	19
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5142 ?
Unique (%)	100.0%

Sample

1st row	WOS:000359863700211
2nd row	WOS:000419747600019
3rd row	WOS:000518574900075
4th row	WOS:000462080100026
5th row	WOS:000322345500073

Value	Count	Frequency (%)
wos:001265872600093	1	< 0.1%
wos:001388694200001	1	< 0.1%
wos:000359863700211	1	< 0.1%
wos:000419747600019	1	< 0.1%
wos:000518574900075	1	< 0.1%
wos:000462080100026	1	< 0.1%
wos:001198138400001	1	< 0.1%
wos:001303612400181	1	< 0.1%
wos:000558088806030	1	< 0.1%
wos:000447408802079	1	< 0.1%
Other values (5132)	5132	99.8%

Most occurring characters

Value	Count	Frequency (%)
0	34601	35.4%
1	8408	8.6%
W	5142	5.3%
S	5142	5.3%
O	5142	5.3%
:	5142	5.3%
4	5024	5.1%
2	4593	4.7%
5	4504	4.6%
3	4471	4.6%
Other values (9)	15529	15.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	97698	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	34601	35.4%
1	8408	8.6%
W	5142	5.3%
S	5142	5.3%
O	5142	5.3%
:	5142	5.3%
4	5024	5.1%
2	4593	4.7%
5	4504	4.6%
3	4471	4.6%
Other values (9)	15529	15.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	97698	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	34601	35.4%
1	8408	8.6%
W	5142	5.3%
S	5142	5.3%
O	5142	5.3%
:	5142	5.3%
4	5024	5.1%
2	4593	4.7%
5	4504	4.6%
3	4471	4.6%
Other values (9)	15529	15.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	97698	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	34601	35.4%
1	8408	8.6%
W	5142	5.3%
S	5142	5.3%
O	5142	5.3%
:	5142	5.3%
4	5024	5.1%
2	4593	4.7%
5	4504	4.6%
3	4471	4.6%
Other values (9)	15529	15.9%

Web of Science Record
Categorical

Constant

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	251.2 KiB

0	5142

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	5142
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	5142	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	5142	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	5142	100.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	5142	100.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	5142	100.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5142	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	5142	100.0%

doi_norm
Text

Missing

Distinct	4191
Distinct (%)	> 99.9%
Missing	950
Missing (%)	18.5%
Memory size	333.0 KiB

Length

Max length	55
Median length	43
Mean length	25.056536
Min length	13

Characters and Unicode

Total characters	105037
Distinct characters	43
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4190 ?
Unique (%)	> 99.9%

Sample

1st row	10.1007/978-3-319-52691-1_18
2nd row	10.1145/3345120.3345134
3rd row	10.1109/ic3.2018.00-45
4th row	10.1145/3629296.3629361
5th row	10.1145/3649217.3653565

Value	Count	Frequency (%)
10.1145/3408877.3439639	2	< 0.1%
10.1080/03057267.2021.1963580	1	< 0.1%
10.1145/3514262.3514311	1	< 0.1%
10.1109/weit.2013.27	1	< 0.1%
10.1007/978-3-031-38454-7_12	1	< 0.1%
10.1109/icetc.2009.16	1	< 0.1%
10.4028/www.scientific.net/amm.373-375.2200	1	< 0.1%
10.1002/cae.22669	1	< 0.1%
10.1145/3345120.3345134	1	< 0.1%
10.1007/s10639-022-11454-1	1	< 0.1%
Other values (4181)	4181	99.7%

Most occurring characters

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (33)	22448	21.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (33)	22448	21.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (33)	22448	21.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	105037	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	16924	16.1%
0	16709	15.9%
.	8905	8.5%
2	8130	7.7%
3	7450	7.1%
9	5354	5.1%
5	5080	4.8%
4	4866	4.6%
7	4849	4.6%
8	4322	4.1%
Other values (33)	22448	21.4%

Interactions

Correlations

	180 Day Usage Count	Book DOI	Book Group Authors	Cited Reference Count	Document Type	Language	Number of Pages	Open Access Designations	Publication Type	Publication Year	Pubmed Id	Since 2013 Usage Count	Special Issue	Supplement	Times Cited, All Databases	Times Cited, WoS Core	Unnamed: 0	Web of Science Index
180 Day Usage Count	1.000	1.000	1.000	0.565	0.029	0.000	0.532	0.135	0.072	0.516	0.344	0.663	0.000	1.000	0.263	0.248	-0.038	0.088
Book DOI	1.000	1.000	1.000	0.242	0.242	1.000	0.769	0.791	0.769	0.792	0.000	1.000	0.000	0.000	1.000	1.000	0.248	0.775
Book Group Authors	1.000	1.000	1.000	0.000	0.728	0.214	1.000	0.102	0.445	0.381	0.000	1.000	0.000	0.000	1.000	1.000	0.038	0.829
Cited Reference Count	0.565	0.242	0.000	1.000	0.249	0.000	0.773	0.068	0.256	0.487	0.152	0.508	0.000	0.000	0.331	0.318	0.105	0.211
Document Type	0.029	0.242	0.728	0.249	1.000	0.000	0.331	0.129	0.822	0.211	0.294	0.104	0.000	0.000	0.096	0.092	0.098	0.437
Language	0.000	1.000	0.214	0.000	0.000	1.000	0.000	0.083	0.043	0.048	1.000	0.000	0.000	0.000	0.000	0.000	0.031	0.065
Number of Pages	0.532	0.769	1.000	0.773	0.331	0.000	1.000	0.000	0.102	0.472	0.135	0.488	0.000	1.000	0.247	0.231	0.107	0.117
Open Access Designations	0.135	0.791	0.102	0.068	0.129	0.083	0.000	1.000	0.242	0.214	0.369	0.126	1.000	1.000	0.000	0.000	0.036	0.223
Publication Type	0.072	0.769	0.445	0.256	0.822	0.043	0.102	0.242	1.000	0.291	0.536	0.149	1.000	1.000	0.000	0.000	0.122	0.791
Publication Year	0.516	0.792	0.381	0.487	0.211	0.048	0.472	0.214	0.291	1.000	0.959	0.081	0.184	0.000	-0.258	-0.263	0.065	0.229
Pubmed Id	0.344	0.000	0.000	0.152	0.294	1.000	0.135	0.369	0.536	0.959	1.000	-0.357	1.000	0.000	-0.621	-0.624	-0.136	0.370
Since 2013 Usage Count	0.663	1.000	1.000	0.508	0.104	0.000	0.488	0.126	0.149	0.081	-0.357	1.000	0.072	0.000	0.636	0.616	-0.106	0.154
Special Issue	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	0.184	1.000	0.072	1.000	1.000	1.000	1.000	0.000	0.000
Supplement	1.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	1.000	0.000	0.000	0.000	1.000	1.000	1.000	1.000	0.378	0.000
Times Cited, All Databases	0.263	1.000	1.000	0.331	0.096	0.000	0.247	0.000	0.000	-0.258	-0.621	0.636	1.000	1.000	1.000	0.984	-0.024	0.102
Times Cited, WoS Core	0.248	1.000	1.000	0.318	0.092	0.000	0.231	0.000	0.000	-0.263	-0.624	0.616	1.000	1.000	0.984	1.000	-0.019	0.094
Unnamed: 0	-0.038	0.248	0.038	0.105	0.098	0.031	0.107	0.036	0.122	0.065	-0.136	-0.106	0.000	0.378	-0.024	-0.019	1.000	0.087
Web of Science Index	0.088	0.775	0.829	0.211	0.437	0.065	0.117	0.223	0.791	0.229	0.370	0.154	0.000	0.000	0.102	0.094	0.087	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	Unnamed: 0	Publication Type	Authors	Book Authors	Book Editors	Book Group Authors	Author Full Names	Book Author Full Names	Group Authors	Article Title	Source Title	Book Series Title	Book Series Subtitle	Language	Document Type	Conference Title	Conference Date	Conference Location	Conference Sponsor	Conference Host	Author Keywords	Keywords Plus	Abstract	Addresses	Affiliations	Reprint Addresses	Email Addresses	Researcher Ids	ORCIDs	Funding Orgs	Funding Name Preferred	Funding Text	Cited References	Cited Reference Count	Times Cited, WoS Core	Times Cited, All Databases	180 Day Usage Count	Since 2013 Usage Count	Publisher	Publisher City	Publisher Address	ISSN	eISSN	ISBN	Journal Abbreviation	Journal ISO Abbreviation	Publication Date	Publication Year	Volume	Issue	Part Number	Supplement	Special Issue	Meeting Abstract	Start Page	End Page	Article Number	DOI	DOI Link	Book DOI	Early Access Date	Number of Pages	WoS Categories	Web of Science Index	Research Areas	IDS Number	Pubmed Id	Open Access Designations	Highly Cited Status	Hot Paper Status	Date of Export	UT (Unique WOS ID)	doi_norm
0	0	C	WeiguoZou	NaN	Kim, Y	NaN	WeiguoZou	NaN	NaN	Computational Thinking Ability Training in College Computer Teaching	PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND TECHNOLOGY EDUCATION (ICSSTE 2015)	Advances in Social Science Education and Humanities Research	NaN	English	Proceedings Paper	International Conference on Social Science and Technology Education (ICSSTE)	APR 11-12, 2015	Sanya, PEOPLES R CHINA	Int Assoc Cyber Sci & Engn	NaN	Computational Thinking; Computer Teaching; Blended Teaching Model	NaN	Computational thinking is ubiquitous, and has become a hot spot in education. As the core mission of college computer teaching, computational thinking training naturally causes widespread concern in the field of basic computer education. In this study, we propose the computational thinking formation procedural model, which reveals that computational thinking is the unity of internal and process, and the computational thinking ability training process is an important reason for the formation of computational thinking ability. And based on the factors of computational thinking training process, we construct the computational thinking - based blended teaching model.	Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China	NaN	WeiguoZou (autor correspondiente), Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5	1	1	2	38	ATLANTIS PRESS	PARIS	29 AVENUE LAVMIERE, PARIS, 75019, FRANCE	2352-5398	NaN	978-94-62520-60-8	ADV SOC SCI EDUC HUM	NaN	NaN	2015	18	NaN	NaN	NaN	NaN	NaN	817	821	NaN	NaN	NaN	NaN	NaN	5	Education & Educational Research; Social Sciences, Interdisciplinary	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Education & Educational Research; Social Sciences - Other Topics	BD3LM	NaN	NaN	NaN	NaN	2025-12-30	WOS:000359863700211	NaN
1	1	B	Repenning, A; Basawapatna, AR; Escherle, NA	NaN	Rich, PJ; Hodges, CB	NaN	Repenning, Alexander; Basawapatna, Ashok R.; Escherle, Nora A.	NaN	NaN	Principles of Computational Thinking Tools	EMERGING RESEARCH, PRACTICE, AND POLICY ON COMPUTATIONAL THINKING	Educational Communications and Technology-Issues and Innovations	NaN	English	Article; Book Chapter	NaN	NaN	NaN	NaN	NaN	Computational Thinking Process; Three stages of the Computational Thinking Process; Computational Thinking Tools; Principles of Computational Thinking Tools	END	Computational Thinking is a fundamental skill for the twenty-first century workforce. This broad target audience, including teachers and students with no programming experience, necessitates a shift in perspective toward Computational Thinking Tools that not only provide highly accessible programming environments but explicitly support the Computational Thinking Process. This evolution is crucial if Computational Thinking Tools are to be relevant to a wide range of school disciplines including STEM, art, music, and language learning. Computational Thinking Tools must help users through three fundamental stages of Computational Thinking: problem formulation, solution expression, and execution/evaluation. This chapter outlines three principles, and employs AgentCubes online as an example, on how a Computational Thinking Tool provides support for these stages by unifying human abilities with computer affordances.	[Repenning, Alexander; Escherle, Nora A.] Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland; [Basawapatna, Ashok R.] SUNY Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USA	FHNW University of Applied Sciences & Arts Northwestern Switzerland; State University of New York (SUNY) System; SUNY Old Westbury	Repenning, A (autor correspondiente), Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland.	alexander.repenning@fhnw.ch; basawapatnaa@oldwestbury.edu; nora.escherle@fhnw.ch	NaN	REPENNING, ALEXANDER/0000-0002-2165-7533	NaN	NaN	NaN	NaN	49	16	22	0	58	SPRINGER INTERNATIONAL PUBLISHING AG	CHAM	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND	NaN	NaN	978-3-319-52691-1; 978-3-319-52690-4	EDUC COMMUN TECHNOL	Educ. Commun. Technol.	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	291	305	NaN	10.1007/978-3-319-52691-1_18	0.0	10.1007/978-3-319-52691-1	NaN	15	Education & Educational Research; Social Issues	Book Citation Index– Social Sciences & Humanities (BKCI-SSH)	Education & Educational Research; Social Issues	BJ2PE	NaN	NaN	NaN	NaN	2025-12-30	WOS:000419747600019	10.1007/978-3-319-52691-1_18
2	2	C	Shih, WC	NaN	NaN	Assoc Comp Machinery	Shih, Wen-Chung	NaN	NaN	Integrating Computational Thinking into the Process of Learning Artificial Intelligence	ICEMT 2019: 2019 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND MULTIMEDIA TECHNOLOGY	NaN	NaN	English	Proceedings Paper	3rd International Conference on Education and Multimedia Technology (ICEMT)	JUL 22-25, 2019	Nagoya, JAPAN	NaN	NaN	Computational thinking; Experiential learning; Artificial intelligence	NaN	In recent years, computational thinking has once again received attention widely. Computational thinking is generally considered to be the ability to be acquired. However, this study is to use computational thinking as part of the learning method. In order to explore the application of computational thinking in teaching, this study first collected the main review papers, as well as the literature on the assessment of computational thinking, and examined their views. Then, this study proposes a learning method that integrates computational thinking into experiential learning theory and applies it to learning artificial intelligence techniques.	[Shih, Wen-Chung] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan	Asia University Taiwan	Shih, WC (autor correspondiente), Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.	wjshih@asia.edu.tw	NaN	Shih, Wen-Chung/0000-0003-4838-8473	Ministry of Science and Technology of Republic of China [MOST 106-2511-S-468 -005 -MY2]	Ministry of Science and Technology of Republic of China(Ministry of Science and Technology, Taiwan)	This research was supported by Ministry of Science and Technology of Republic of China under the number of MOST 106-2511-S-468 -005 -MY2.	NaN	28	4	5	3	67	ASSOC COMPUTING MACHINERY	NEW YORK	1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES	NaN	NaN	978-1-4503-7210-7	NaN	NaN	NaN	2019	NaN	NaN	NaN	NaN	NaN	NaN	364	368	NaN	10.1145/3345120.3345134	0.0	NaN	NaN	5	Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods	Conference Proceedings Citation Index - Science (CPCI-S)	Computer Science	BO5OP	NaN	NaN	NaN	NaN	2025-12-30	WOS:000518574900075	10.1145/3345120.3345134
3	3	C	Wu, SY	NaN	NaN	IEEE	Wu, Sheng-Yi	NaN	NaN	The Development and Challenges of Computational Thinking Board Games	2018 FIRST INTERNATIONAL COGNITIVE CITIES CONFERENCE (IC3 2018)	NaN	NaN	English	Proceedings Paper	1st International Cognitive Cities Conference (IC3)	AUG 07-09, 2018	Okinawa, JAPAN	IEEE Comp Soc,Okinawa Inst Sci & Technol	NaN	computational thinking; board game; coding; game-based learning	NaN	The promotion of computational thinking education has become a worldwide trend. To cultivate the computational thinking ability of children at young age, many computational thinking board games have appeared recently. This article introduces five computational thinking board games, including Robot Turtles, King of Pirates, Doggy Code, ROBOT WARS Coding Board Game, and Code master, and then to analyze its characteristics respectively. Additionally, this article also points out the current limitations and challenges of computational thinking board games. We hope more schools or operators will join the development of computational thinking education in the future.	[Wu, Sheng-Yi] Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan	National Pingtung University	Wu, SY (autor correspondiente), Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan.	digschool@gmail.com	Wu, Sheng-Yi/C-4143-2011	Wu, Sheng-Yi/0000-0003-3022-1843	NaN	NaN	NaN	NaN	11	7	8	1	37	IEEE	NEW YORK	345 E 47TH ST, NEW YORK, NY 10017 USA	NaN	NaN	978-1-5386-5059-2	NaN	NaN	NaN	2018	NaN	NaN	NaN	NaN	NaN	NaN	129	131	NaN	10.1109/IC3.2018.00-45	0.0	NaN	NaN	3	Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic	Conference Proceedings Citation Index - Science (CPCI-S)	Computer Science; Engineering	BM3GW	NaN	NaN	NaN	NaN	2025-12-30	WOS:000462080100026	10.1109/ic3.2018.00-45
4	4	C	Lu, CJ; Zhang, S; Chen, XQ	NaN	Chang, T	NaN	Lu Changjin; Zhang Shuai; Chen Xiuqiong	NaN	NaN	The Study on Computational Thinking	2012 INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MANAGEMENT INNOVATION (ERMI 2012), VOL 1	NaN	NaN	English	Proceedings Paper	International Conference on Education Reform and Management Innovation (ERMI 2012)	DEC 04-05, 2012	Shenzhen, PEOPLES R CHINA	NaN	NaN	information process; computational thinking; talent-training strategy	NaN	This article makes an introduction to the research of computational thinking on its form, characteristic and present situation, analyses the background and the reasons for the rise of computational thinking, draws the conclusion that computational thinking should be considered from national strategic level of talent-training, and proposes some countermeasures of computational thinking enhancement, talent-training and information process.	[Lu Changjin; Zhang Shuai; Chen Xiuqiong] Sanming Univ, Dept Math & Comp Sci, Sanming, Fujian Province, Peoples R China	Sanming University	NaN	smlcj123@163.com	NaN	NaN	NaN	NaN	NaN	NaN	3	0	0	0	21	INFORMATION ENGINEERING RESEARCH INST, USA	NEWARK	100 CONTINENTAL DR, NEWARK, DE 19713 USA	NaN	NaN	978-1-16275-049-1	NaN	NaN	NaN	2013	NaN	NaN	NaN	NaN	NaN	NaN	376	380	NaN	NaN	NaN	NaN	NaN	5	Economics; Education & Educational Research; Management	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Business & Economics; Education & Educational Research	BGC87	NaN	NaN	NaN	NaN	2025-12-30	WOS:000322345500073	NaN
5	5	C	Zhang, W; Song, LL; Huang, XJ; Wang, Y	NaN	ACM	NaN	Zhang, Wei; Song, Lingling; Huang, Xujun; Wang, Yi	NaN	NaN	Construction and Practice of Computational Thinking Structural Framework with Sternberg's Intellectual Education Theory	PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY AND COMPUTERS, ICETC 2023	NaN	NaN	English	Proceedings Paper	15th International Conference on Education Technology and Computers (ICETC)	SEP 26-28, 2023	Univ Barcelona, Barcelona, SPAIN	Grup Recerca Ensenyament Aprenentatge Virtual,Univ Warwick,Tecnologico Monterrey	Univ Barcelona	computational thinking; intelligence theories; software development; technology; project-based teaching	NaN	Cultivating students' computational thinking skills is one of the important teaching goals of college computer courses, and constructing a scientific and effective structural framework for computational thinking is the basis for implementing computational thinking training. To this end, based on Sternberg's intelligence education theory, combined with software development and programming knowledge, a structural framework of computational thinking was constructed. Subsequently, a project-based teaching activity based on this framework to cultivate students' computational thinking skills was carried out in the college computer programming course Software Development Technology. By measuring students' computational thinking skills before and after the activity, it was verified that the project-based teaching model based on the proposed framework had a significant effect on improving students' computational thinking skills. By constructing a structural framework of computational thinking that integrates the characteristics of disciplines, and taking programming teaching as a carrier to implement the cultivation of computational thinking skills, it can provide a new way for computer educators to cultivate students' computational thinking skills.	[Zhang, Wei; Song, Lingling] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China; [Huang, Xujun] Zhongnan Univ Econ & Law, Sch Foreign Studies, Wuhan, Peoples R China; [Wang, Yi] Guiyang Big Data Applicat Serv Ctr, Guiyang, Peoples R China	Central China Normal University; Zhongnan University of Economics & Law	Song, LL (autor correspondiente), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.	zwccnu@ccnu.edu.cn; linglingsong@mails.ccnu.edu.cn; hxj168168@zuel.edu.cn; wlqyy_fam@163.com	NaN	NaN	National Natural Science Foundation of China [61977031]	National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC))	This work has been finally supported by the National Natural Science Foundation of China (Grant No.61977031) named Research on the Intelligent Comprehensive Assessment of Computational Thinking for the Key Competence.	NaN	14	0	0	4	15	ASSOC COMPUTING MACHINERY	NEW YORK	1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES	NaN	NaN	979-8-4007-0911-1	NaN	NaN	NaN	2023	NaN	NaN	NaN	NaN	NaN	NaN	404	408	NaN	10.1145/3629296.3629361	0.0	NaN	NaN	5	Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific Disciplines	Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Computer Science; Education & Educational Research	BW5RO	NaN	NaN	NaN	NaN	2025-12-30	WOS:001166851900062	10.1145/3629296.3629361
6	6	C	Pasterk, S; Benke, G	NaN	NaN	ACM	Pasterk, Stefan; Benke, Gertraud	NaN	NaN	Computational Thinking for Self-Regulated Learning	PROCEEDINGS OF THE 2024 CONFERENCE INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, VOL 1, ITICSE 2024	NaN	NaN	English	Proceedings Paper	29th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE)	JUL 08-10, 2024	Univ Milano, Milan, ITALY	Assoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Comp Sci Educ,ACM Europe Council,Informat Europe,Github Educ,Univ Milano, Dipartimento Informatica	Univ Milano	self-regulated learning; computational thinking; secondary school	NaN	In this theoretical paper, we compare computational thinking and self-regulated learning. Many studies use self-regulated learning to foster the acquisition of computational thinking competencies. Self-regulated learning skills are themselves beneficial for any learning process; here, we argue that the relationship between self-regulated learning and computational thinking is closer than the simple observation that self-regulated learning strategies support the acquisition of computational thinking competencies. We sustain that self-regulated learning and computational thinking competencies share many features (and have some differences), which would support synergistic effects so that not only can self-regulated learning be used to attain computational thinking competencies, but computational thinking activities can also be used to foster features of self-regulated learning competencies.	[Pasterk, Stefan; Benke, Gertraud] Univ Klagenfurt, Klagenfurt, Austria	University of Klagenfurt	Pasterk, S (autor correspondiente), Univ Klagenfurt, Klagenfurt, Austria.	stefan.pasterk@aau.at; gertraud.benke@aau.at	NaN	Benke, Gertraud/0000-0002-6710-191X	NaN	NaN	NaN	NaN	35	1	1	3	24	ASSOC COMPUTING MACHINERY	NEW YORK	1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES	NaN	NaN	979-8-4007-0600-4	NaN	NaN	NaN	2024	NaN	NaN	NaN	NaN	NaN	NaN	640	645	NaN	10.1145/3649217.3653565	0.0	NaN	NaN	6	Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific Disciplines	Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Computer Science; Education & Educational Research	BX2PG	NaN	NaN	NaN	NaN	2025-12-30	WOS:001265872600093	10.1145/3649217.3653565
7	7	C	Chen, L; Xia, JX; Tao, J	NaN	NaN	IEEE	Chen, Li; Xia, Jiaoxiong; Tao, Jie	NaN	NaN	Cultivating Computational Thinking Among Students Of Liberal Art In Basic Computer Courses	2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI)	International Conference on Systems and Informatics	NaN	English	Proceedings Paper	5th International Conference on Systems and Informatics (ICSAI)	NOV 10-12, 2018	Nanjing, PEOPLES R CHINA	Shanghai Dianji Univ, Sch Elect & Informat,IEEE Syst, Man, & Cybernet Soc	NaN	Computational Thinking; Computer Foundation; Students of Liberal Art; Educational Reform	NaN	With the concept of Computational Thinking proposed, how to cultivate student's Computational Thinking in basic computer courses has become the focus of basic computer education reform in recent years. Computational Thinking has gradually been recognized by computer educators and has become an important goal of basic computer education. According to the author's experience, the paper analyzes the reasons why students of literal arts also need to acquire Computational Thinking after summarizing the current research status of Computational Thinking at home and abroad. Regarding the cultivation of Computational Thinking ability as a higher level than knowledge learning and skill training, the paper puts forward some improving suggestions on how to cultivate Computational Thinking among students of liberal arts.	[Chen, Li; Xia, Jiaoxiong; Tao, Jie] Shanghai Int Studies Univ, Xianda Coll Econ & Humanities, Shanghai 200083, Peoples R China	Shanghai International Studies University; Xianda College of Economics & Humanities Shanghai International Studies University	Chen, L (autor correspondiente), Shanghai Int Studies Univ, Xianda Coll Econ & Humanities, Shanghai 200083, Peoples R China.	NaN	NaN	NaN	Second Round of Research Projects for Shanghai Private Colleges [2016-SHNGE-08ZD]	Second Round of Research Projects for Shanghai Private Colleges	This Research was financially supported by the Second Round of Research Projects for Shanghai Private Colleges (2016-SHNGE-08ZD, Thanks for the help.	NaN	8	0	0	0	16	IEEE	NEW YORK	345 E 47TH ST, NEW YORK, NY 10017 USA	2474-0217	NaN	978-1-7281-0120-0	INT CONF SYST INFORM	NaN	NaN	2018	NaN	NaN	NaN	NaN	NaN	NaN	544	548	NaN	NaN	NaN	NaN	NaN	5	Engineering, Electrical & Electronic	Conference Proceedings Citation Index - Science (CPCI-S)	Engineering	BM1JJ	NaN	NaN	NaN	NaN	2025-12-30	WOS:000459881500096	NaN
8	8	C	Repenning, A; Basawapatna, A; Escherle, N	NaN	Blackwell, A; Plimmer, B; Stapleton, G	NaN	Repenning, Alexander; Basawapatna, Ashok; Escherle, Nora	NaN	NaN	Computational Thinking Tools	2016 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC)	Symposium on Visual Languages and Human Centric Computing VL HCC	NaN	English	Proceedings Paper	IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)	SEP 04-08, 2016	Cambridge, ENGLAND	IEEE	NaN	computational thinking tools; end-user programming; K-12 education; computational thinking	NaN	Computational Thinking is an essential skill for all students in the 21st Century. A fundamental question is how can we create computer affordances to empower novice teachers and students, in a variety of STEM and art disciplines, to think computationally while avoiding difficult overhead emerging from traditional coding? Over the last 20 years we have iteratively developed tools that aim to support computational thinking. As these tools evolved a philosophy emerged to support Computational Thinking by joining human abilities with computer affordances. Chief among these findings is that supporting Computational Thinking is much more than making coding accessible. Computational Thinking Tools aim to minimize coding overhead by supporting users through three fundamental stages of the Computational Thinking development cycle: problem formulation, solution expression, and solution execution/evaluation.	[Repenning, Alexander] Univ Colorado, Boulder, CO 80309 USA; [Repenning, Alexander; Escherle, Nora] FHNW, Sch Educ, CH-5200 Brugge, Switzerland; [Basawapatna, Ashok] SUNY Coll Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USA	University of Colorado System; University of Colorado Boulder; State University of New York (SUNY) System; SUNY Old Westbury	Repenning, A (autor correspondiente), Univ Colorado, Boulder, CO 80309 USA.;Repenning, A (autor correspondiente), FHNW, Sch Educ, CH-5200 Brugge, Switzerland.	NaN	NaN	REPENNING, ALEXANDER/0000-0002-2165-7533	Hasler Foundation; National Science Foundation [0833612, 1345523, 0848962]; Direct For Education and Human Resources; Division Of Research On Learning [0833612] Funding Source: National Science Foundation; Directorate For Engineering [0848962, 1345523] Funding Source: National Science Foundation; Div Of Industrial Innovation & Partnersh [1345523, 0848962] Funding Source: National Science Foundation	Hasler Foundation; National Science Foundation(National Science Foundation (NSF)); Direct For Education and Human Resources; Division Of Research On Learning(National Science Foundation (NSF)NSF - Directorate for STEM Education (EDU)); Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)); Div Of Industrial Innovation & Partnersh(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG))	This work is supported by the Hasler Foundation and the National Science Foundation under Grant Numbers 0833612, 1345523, and 0848962. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these foundations.	NaN	37	27	35	0	55	IEEE	NEW YORK	345 E 47TH ST, NEW YORK, NY 10017 USA	1943-6092	NaN	978-1-5090-0252-8	S VIS LANG HUM CEN C	NaN	NaN	2016	NaN	NaN	NaN	NaN	NaN	NaN	218	222	NaN	NaN	NaN	NaN	NaN	5	Computer Science, Software Engineering; Computer Science, Theory & Methods	Conference Proceedings Citation Index - Science (CPCI-S)	Computer Science	BG8AX	NaN	NaN	NaN	NaN	2025-12-30	WOS:000392158000034	NaN
9	9	J	Ezeamuzie, NO; Leung, JSC	NaN	NaN	NaN	Ezeamuzie, Ndudi O.; Leung, Jessica S. C.	NaN	NaN	Computational Thinking Through an Empirical Lens: A Systematic Review of Literature	JOURNAL OF EDUCATIONAL COMPUTING RESEARCH	NaN	NaN	English	Review	NaN	NaN	NaN	NaN	NaN	computational thinking; problem solving; algorithms; programming; abstraction	ELEMENTARY CLASSROOMS; COMPUTER-SCIENCE; ROBOTICS; SKILLS; PAPER; EXPLORATION; VIEWPOINT; EDUCATION; VALIDITY; DESIGN	This article provides an overview of the diverse ways in which computational thinking has been operationalised in the literature. Computational thinking has attracted much interest and debatably ranks in importance with the time-honoured literacy skills of reading, writing, and arithmetic. However, learning interventions in this subject have modelled computational thinking differently. We conducted a systematic review of 81 empirical studies to examine the nature, explicitness, and patterns of definitions of computational thinking. Data analysis revealed that most of the reviewed studies operationalised computational thinking as a composite of programming concepts and preferred definitions from assessment-based frameworks. On the other hand, a substantial number of the studies did not establish the meaning of computational thinking when theorising their interventions nor clearly distinguish between computational thinking and programming. Based on these findings, this article proposes a model of computational thinking that focuses on algorithmic solutions supported by programming concepts which advances the conceptual clarity between computational thinking and programming.	[Ezeamuzie, Ndudi O.; Leung, Jessica S. C.] Univ Hong Kong, Fac Educ, Hong Kong, Hong Kong, Peoples R China	University of Hong Kong	Ezeamuzie, NO (autor correspondiente), Univ Hong Kong, Fac Educ, Hong Kong, Hong Kong, Peoples R China.	amuzie@connect.hku.hk	Leung, Jessica Shuk Ching/G-4619-2013; Ezeamuzie, Ndudi Okechukwu/ABG-1289-2021	Ezeamuzie, Ndudi Okechukwu/0000-0001-8946-5709	NaN	NaN	NaN	NaN	100	68	86	29	393	SAGE PUBLICATIONS INC	THOUSAND OAKS	2455 TELLER RD, THOUSAND OAKS, CA 91320 USA	0735-6331	1541-4140	NaN	J EDUC COMPUT RES	J. Educ. Comput. Res.	APR	2022	60	2	NaN	NaN	NaN	NaN	481	511	7356331211033158	10.1177/07356331211033158	0.0	NaN	JUL 2021	31	Education & Educational Research	Social Science Citation Index (SSCI)	Education & Educational Research	0H2KI	NaN	NaN	NaN	NaN	2025-12-30	WOS:000678265200001	10.1177/07356331211033158

	Unnamed: 0	Publication Type	Authors	Book Authors	Book Editors	Book Group Authors	Author Full Names	Book Author Full Names	Group Authors	Article Title	Source Title	Book Series Title	Book Series Subtitle	Language	Document Type	Conference Title	Conference Date	Conference Location	Conference Sponsor	Conference Host	Author Keywords	Keywords Plus	Abstract	Addresses	Affiliations	Reprint Addresses	Email Addresses	Researcher Ids	ORCIDs	Funding Orgs	Funding Name Preferred	Funding Text	Cited References	Cited Reference Count	Times Cited, WoS Core	Times Cited, All Databases	180 Day Usage Count	Since 2013 Usage Count	Publisher	Publisher City	Publisher Address	ISSN	eISSN	ISBN	Journal Abbreviation	Journal ISO Abbreviation	Publication Date	Publication Year	Volume	Issue	Part Number	Supplement	Special Issue	Meeting Abstract	Start Page	End Page	Article Number	DOI	DOI Link	Book DOI	Early Access Date	Number of Pages	WoS Categories	Web of Science Index	Research Areas	IDS Number	Pubmed Id	Open Access Designations	Highly Cited Status	Hot Paper Status	Date of Export	UT (Unique WOS ID)	doi_norm
5132	5132	J	Goldenberg, EP; Carter, CJ	NaN	NaN	NaN	Goldenberg, E. Paul; Carter, Cynthia J.	NaN	NaN	Programming as a language for young children to express and explore mathematics in school	BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	activity‐ based learning; coding; constructionism; mathematics; primary education	EARLY-CHILDHOOD MATHEMATICS	Natural language helps express mathematical thinking and contexts. Conventional mathematical notation (CMN) best suits expressions and equations. Each is essential; each also has limitations, especially for learners. Our research studies how programming can be a advantageous third language that can also help restore mathematical connections that are hidden by topic-centred curricula. Restoring opportunities for surprise and delight reclaims mathematics' creative nature. Studies of children's use of language in mathematics and their programming behaviours guide our iterative design/redesign of mathematical microworlds in which students, ages 7-11, use programming in their regular school lessons as a language for learning mathematics. Though driven by mathematics, not coding, the microworlds develop the programming over time so that it continues to support children's developing mathematical ideas. This paper briefly describes microworlds EDC has tested with well over 400 7-to-8-year-olds in school, and others tested (or about to be tested) with over 200 8-to-11-year-olds. Our challenge was to satisfy schools' topical orientation and fit easily within regular classroom study but use and foreshadow other mathematical learning to remove the siloes. The design/redesign research and evaluation is exploratory, without formal methodology. We are also more formally studying effects on children's learning. That ongoing study is not reported here. Practitioner notes What is already known Active learning-doing-supports learning. Collaborative learning-doing together-supports learning. Classroom discourse-focused, relevant discussion, not just listening-supports learning. Clear articulation of one's thinking, even just to oneself, helps develop that thinking. What this paper adds The common languages we use for classroom mathematics-natural language for conveying the meaning and context of mathematical situations and for explaining our reasoning; and the formal (written) language of conventional mathematical notation, the symbols we use in mathematical expressions and equations-are both essential but each presents hurdles that necessitate the other. Yet, even together, they are insufficient especially for young learners. Programming, appropriately designed and used, can be the third language that both reduces barriers and provides the missing expressive and creative capabilities children need. Appropriate design for use in regular mathematics classrooms requires making key mathematical content obvious, strong and the 'driver' of the activities, and requires reducing tech 'overhead' to near zero. Continued usefulness across the grades requires developing children's sophistication and knowledge with the language; the powerful ways that children rapidly acquire facility with (natural) language provides guidance for ways they can learn a formal language as well. Implications for policy and/or practice Mathematics teaching can take advantage of the ways children learn through experimentation and attention to the results, and of the ways children use their language brain even for mathematics. In particular, programming-in microworlds driven by the mathematical content, designed to minimise distraction and overhead, open to exploration and discovery en route to focused aims, and in which children self-evaluate-can allow clear articulation of thought, experimentation with immediate feedback. As it aids the mathematics, it also builds computational thinking and satisfies schools' increasing concerns to broaden access to ideas of computer science.	[Goldenberg, E. Paul] Educ Dev Ctr EDC, 43 Foundry Ave, Waltham, MA 02453 USA; [Carter, Cynthia J.] Rashi Sch, Dedham, MA USA	Education Development Center (EDC)	Goldenberg, EP (autor correspondiente), Educ Dev Ctr EDC, 43 Foundry Ave, Waltham, MA 02453 USA.	pgoldenberg@edc.org	NaN	NaN	NaN	NaN	NaN	NaN	38	11	18	7	64	WILEY	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	0007-1013	1467-8535	NaN	BRIT J EDUC TECHNOL	Br. J. Educ. Technol.	MAY	2021	52	3	NaN	NaN	NaN	NaN	969	985	NaN	10.1111/bjet.13080	0.0	NaN	NaN	17	Education & Educational Research	Social Science Citation Index (SSCI)	Education & Educational Research	SK0OC	NaN	hybrid	NaN	NaN	2025-12-30	WOS:000655921600002	10.1111/bjet.13080
5133	5133	J	Rahimi, S; Almond, R; Ramírez-Salgado, A; Wusylko, C; Weisberg, L; Song, Y; Lu, J; Myers, T; Wang, BW; Wang, XM; Francois, M; Moses, J; Wright, E	NaN	NaN	NaN	Rahimi, Seyedahmad; Almond, Russell; Ramirez-Salgado, Andrea; Wusylko, Christine; Weisberg, Lauren; Song, Yukyeong; Lu, Jie; Myers, Ted; Wang, Bowen; Wang, Xiaomaon; Francois, Marc; Moses, Jennifer; Wright, Eric	NaN	NaN	Competency model development: The backbone of successful stealth assessments	JOURNAL OF COMPUTER ASSISTED LEARNING	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	assessment for learning; competency model; digital games; evidence cantered design; learning analytics; stealth assessment	DIVERGENT THINKING; CREATIVITY	BackgroundStealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real-time inferences about their learning. Employed in digital learning environments, stealth assessment helps researchers, educators, and teachers evaluate learners' competencies and customize the learning experience to their specific needs. This adaptability is closely intertwined with theories related to learning, engagement, and motivation. The foundation of stealth assessment rests on evidence-cantered design (ECD), consisting of four core models: the Competency Model (CM), Evidence Model, Task Model, and Assembly Model.ObjectiveThe first step in designing a stealth assessment entails producing operational definitions of the constructs to be assessed. The CM establishes a framework of latent variables representing the target constructs, as well as their interrelations. When developing the CM, assessment designers must produce clear descriptions of the claims associated with the latent variables and their states, as well as sketch out how the competencies can be measured using assessment tasks. As the designers elaborate on the assessment model, the CM definitions need to be revisited to make sure they work with the scope and constraints of the assessment. Although this is the first step, problems at this stage may result in an assessment that does not meet the intended purpose. The objective of this paper is to elucidate the necessary steps for CM development and to highlight potential challenges in the process, along with strategies for addressing them, particularly for designers without much formal assessment experience.MethodThis paper is a methodological exposition, showcasing five examples of CM development. Specifically, we conducted a qualitative retrospective analysis of the CM development procedure, wherein participants unfamiliar with ECD applied the framework and showcased their work. In a stealth assessment course, four groups of students (novice stealth assessment designers) engaged in developing stealth assessments for challenging-to-measure constructs across four distinct projects. During their CM development process, we observed various activities to pinpoint areas of difficulty.ResultsThis paper presents five illustrative examples, including one for assessing physics understanding and four for the development of CMs for four complex competencies: (1) systems thinking, (2) online information credibility evaluation, (3) computational thinking, and (4) collaborative creativity. Each example represents a case in CM development, offering valuable insights.ConclusionThe paper concludes by discussing several guidelines derived from the examples discussed. Emphasizing the importance of dedicating ample time to fine-tune CMs can significantly enhance the accuracy of assessments related to learners' knowledge and skills. It underscores the significance of qualitative phases in crafting comprehensive stealth assessments, such as CMs, alongside the quantitative statistical modeling and technical aspects of these assessments. What is currently known about this topic? Stealth assessment represents an unobtrusive, automated formative assessment method. This method uses learning analytics within digital learning environments (e.g., games). The main purpose is to assess and foster the competencies of diverse learners.What does this paper add? This paper serves as a conceptual and methodological guide. This paper focuses on the critical process of competency model development. Competency model development is a crucial step in the creation of stealth assessments.Implications for practice/or policy Learning scientists and assessment designers can leverage this paper as a resource. Assessment designers can benefit from this paper and see various examples of the process.	[Rahimi, Seyedahmad; Ramirez-Salgado, Andrea; Wusylko, Christine; Weisberg, Lauren; Song, Yukyeong; Lu, Jie; Myers, Ted; Wang, Bowen; Wang, Xiaomaon; Francois, Marc; Moses, Jennifer; Wright, Eric] Univ Florida, Sch Teaching & Learning, Gainesville, FL USA; [Almond, Russell] Florida State Univ, Educ Psychol & Learning Syst, Tallahassee, FL USA; [Rahimi, Seyedahmad] Univ Florida, Coll Educ, Sch Teaching & Learning, 2403 Norman Hall, Gainesville, FL 32611 USA	State University System of Florida; University of Florida; State University System of Florida; Florida State University; State University System of Florida; University of Florida	Rahimi, S (autor correspondiente), Univ Florida, Coll Educ, Sch Teaching & Learning, 2403 Norman Hall, Gainesville, FL 32611 USA.	srahimi@ufl.edu	Weisberg, Lauren/M-1489-2019; Wang, Bowen/KXR-3143-2024; Almond, Russell/GQB-0824-2022; Francois, Marc/JCN-8502-2023; Wusylko, Christine/JLK-9388-2023; Ramirez-Salgado, Andrea/ACY-2548-2022	Weisberg, Lauren/0000-0001-8033-3662; Lu, Jie Jennifer/0000-0002-7466-6177; Rahimi, Seyedahmad/0000-0001-9266-758X; Almond, Russell/0000-0002-8876-9337;	NaN	NaN	NaN	NaN	63	3	3	6	30	WILEY	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	0266-4909	1365-2729	NaN	J COMPUT ASSIST LEAR	J. Comput. Assist. Learn.	DEC	2024	40	6	NaN	NaN	NaN	NaN	2772	2789	NaN	10.1111/jcal.13025	0.0	NaN	JUN 2024	18	Education & Educational Research	Social Science Citation Index (SSCI)	Education & Educational Research	N5B3V	NaN	Bronze	NaN	NaN	2025-12-30	WOS:001243574300001	10.1111/jcal.13025
5134	5134	C	Ronsivalle, GB; Boldi, A; Giunta, E	NaN	Chova, LG; Martinez, AL; Torres, IC	NaN	Ronsivalle, G. B.; Boldi, A.; Giunta, E.	NaN	NaN	IMPROVING THE TRAINING PROCESS: A COURSE TO HELP EDUCATORS LEADING EFFECTIVELY CODING ACTIVITIES	10TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION (ICERI2017)	ICERI Proceedings	NaN	English	Proceedings Paper	10th Annual International Conference of Education, Research and Innovation (ICERI)	NOV 16-18, 2017	Seville, SPAIN	NaN	NaN	Coding; training; educators; computational thinking; innovation; school	NaN	Coding is a powerful instrument to include and integrate the different learning styles and the cognitive peculiarities of every student: writing clear and unequivocal instructions, using a machine language and working with a concrete tool could lead students to be more accurate, concise and conscious about their own process of learning. Coding lessons motivate student learning scientific disciplines, overcoming those emotional factors obstructing a fertile experience of school. Because of the advantages offered, Coding has been fully introduced in ministerial programs in Italy as a discipline: it is not only an innovative subject for pedagogy in theoretical terms, but it requires schools to rapidly and effectively comply with reforms implemented since 2014. OECD TALIS 2013 data confirms teachers need training, as they are required to teach and use Coding as a school discipline, but they do not have the required competencies: based on this, in 2015 the first project was launched to better introduce Coding in the Italian Schools. However, the current training paths which are offered to educators, in Italy and abroad, present several shortcomings: poor timing, absence of evaluation moments and, consequently, lack of tools for analysis of results. In order to overcome these critical issues, we propose and describe the design of a training course addressed to educators. This path is tailored to meet the real needs and the characteristics of the recipients, it refers to a rigorous design method and it is able to quantify the actual increase of the skills of the subjects. This method could support the teachers in organizing educational interventions, avoiding the use of improvisation, increasing the internal validity of the course and allowing replication in other contexts. First of all, we provide an overview of the Coding projects launched in Italian schools: method, structure, monitoring data and first results. Then, we identify the best practices and the lesson learned which are summarized in a guideline. We finally propose a course path, observing some important quality criteria to self-evaluate the quality of our work. In particular, we apply three main quality indicators: 1. The quality of the design: a) preliminary analysis, b) macro-design and c) micro-design; 2. The quality provided: a) pre-delivery and b) delivery; 3. Quality of the results' evaluation: a) evaluation and b) reporting. The course design consists of two essential moments: I) A first phase, macro-design, which illustrates the general structure of the course. The three main outputs of this phase are: 1. the conceptual map, which has been created to identify the conceptual nodes and the main contents of the course; 2. the tree of didactic objectives, each of one is defined according to the Bloom complexity level ( revised): we also list the main assessment tests, consistent with the level of the Bloom's Taxonomy; 3. a flow chart, defining the logical structure, the rules and the time scanning; II) A second phase, micro-design, which describes the storyboard of the classroom and the rating storyboard. Following this method, we believe teachers could put in practice programs which are tailored to their particular class. Going through different experimentations, teachers could also promote the use of coding in interdisciplinary domains that are not yet coded also supporting research in this field.	[Ronsivalle, G. B.] Univ Verona, Verona, Italy; [Boldi, A.] Wemole Srl, Rome, Italy	University of Verona	Ronsivalle, GB (autor correspondiente), Univ Verona, Verona, Italy.	NaN	Boldi, Arianna/ADK-4069-2022	NaN	NaN	NaN	NaN	NaN	14	0	0	0	5	IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT	VALENICA	LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN	2340-1095	NaN	978-84-697-6957-7	ICERI PROC	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	6939	6959	NaN	NaN	NaN	NaN	NaN	21	Education & Educational Research	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Education & Educational Research	BJ9XB	NaN	NaN	NaN	NaN	2025-12-30	WOS:000429975307010	NaN
5135	5135	C	Diaz, L; Foster, T; Barashango, SC	NaN	NaN	ASSOC COMPUTING MACHINERY	Diaz, Lien; Foster, Terry; Barashango, Sababu Chaka	NaN	NaN	Does the Advanced Placement Computer Science (CS) Principles course drive equitable and inclusive CS pedagogy, curriculum, and policy as a means to broaden participation in computing?	PROCEEDINGS OF THE CONFERENCE FOR RESEARCH ON EQUITABLE AND SUSTAINED PARTICIPATION IN ENGINEERING, COMPUTING, AND TECHNOLOGY, RESPECT 2024	NaN	NaN	English	Proceedings Paper	Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)	MAY 16-17, 2024	Atlanta, GA	Assoc Comp Machinery,ACM Special Interest Grp Comp Sci Educ	NaN	Advanced Placement Computer Science Principles; equity; inclusion; CS education policy	NaN	The premise for the development of the Advanced Placement (AP) Computer Science Principles course was aimed at broadening participation in computing, as a high school level CS course. Since AP courses carry credibility with millions of students who take AP Exams as they are recognized with prospects of obtaining a college education, the hope was that the AP CS Principles course would lead to increased participation in AP CS Exams, especially with students historically excluded in CS including girls, Black, Hispanic, and Native American students, as well those with disabilities. The course raises opportunities and access to CS in higher education. The AP CS Principles curriculum framework is used in the development of the Exam which is significant in the creation college credit and placement policies. Nearly 1,300 colleges and universities have created policies providing students with opportunities to receive college credit or placement for scoring a 3 or higher on the AP CS Principles Exam [12]. The AP CS Principles curriculum framework is also used to define the learning outcomes for the course and stands as a pivotal tool in shaping high school CS education pathways to post-secondary introductory CS courses: It was designed to meet rigorous content requirements of an innovative first semester college-level introductory CS course. It exposes students to demanding expectations of building high levels of computational thinking skills and practical applications of programming that are valuable as they advance in their academics. It provides opportunities for students to connect fundamental programming concepts with important topics such as understanding the role of data in programming, and how data is processed and analyzed. AP CS Principles also recognizes the societal impacts of technology and teaches students about ethical considerations that may arise when analyzing bias in technological systems so that students develop a well-rounded perspective on technology's role in society [5]. Additional themes such as the infrastructure of the Internet including networks and protocols are also included. This paper focuses on the vision of the AP CS Principles course underpinnings (a) being engaging and appealing to a wider range of students, (b) making it accessible for a more racially, ethnically, and gender-identity diverse population of high school students, and (c) providing the benefits of the AP label on students' high school transcripts gives them options to consider a pathway into college CS studies with an enhanced admissions appeal, potential academic scholarships, and/or careers in the field. We investigate the structure of the AP CS Principles curriculum framework as a key resource that impacts the kinds of teaching and learning that is promoted in the Course and Exam Description. We discuss our experiences with the imbalanced emphasis on inclusive pedagogy and building community within the classroom to directly increase sense of belonging with students historically excluded from computing. Lastly, while the AP CS Principles Exam continues to flourish in participation numbers, we question the effectiveness of policies to promote broadening participation in computing. We review policies from three different states and discuss how they leverage the AP CS Principles course to promote teacher certification and student enrollment but do not necessarily ensure equitable practices to promote diverse representation in terms of gender, race, socioeconomic background, and disability.	[Diaz, Lien; Foster, Terry; Barashango, Sababu Chaka] Georgia Tech, Constellat Ctr Equ Comp, Atlanta, GA 30332 USA	University System of Georgia; Georgia Institute of Technology	Diaz, L (autor correspondiente), Georgia Tech, Constellat Ctr Equ Comp, Atlanta, GA 30332 USA.	ldiaz@cc.gatech.edu; terry.foster@cc.gatech.edu; sababu.barashango@cc.gatech.edu	NaN	NaN	NaN	NaN	NaN	NaN	17	2	2	1	2	ASSOC COMPUTING MACHINERY	NEW YORK	1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES	NaN	NaN	979-8-4007-0626-4	NaN	NaN	NaN	2024	NaN	NaN	NaN	NaN	NaN	NaN	158	162	NaN	10.1145/3653666.3656281	0.0	NaN	NaN	5	Education, Scientific Disciplines; Social Issues; Social Sciences, Interdisciplinary	Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Education & Educational Research; Social Issues; Social Sciences - Other Topics	BX0CA	NaN	gold	NaN	NaN	2025-12-30	WOS:001227772300024	10.1145/3653666.3656281
5136	5136	C	Ronsivalle, GB; Boldi, A; Bazzi, C	NaN	Chova, LG; Martinez, AL; Torres, IC	NaN	Ronsivalle, G. B.; Boldi, A.; Bazzi, C.	NaN	NaN	EDUCATIONAL TECHNOLOGIES FOR SPECIFIC LEARNING DISORDERS (SLD) IN PRIMARY SCHOOL: A LECTURE OF CODING DESIGNED WITH COMPENSATING WRITING SOFTWARE	10TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION (ICERI2017)	ICERI Proceedings	NaN	English	Proceedings Paper	10th Annual International Conference of Education, Research and Innovation (ICERI)	NOV 16-18, 2017	Seville, SPAIN	NaN	NaN	SLDs; Coding; technology; software; compensatory tools	NaN	The diffusion of Specific Learning Disabilities (SLDs), in an evolving theoretical and clinical scenario, afflicts the ability of experts to diagnose, differentiate and provide early support of students suffering from an SLD. It is necessary to correctly assess the presence of this type of neurodiversity in the learning style since the pupil attends Primary School, limiting the important damage caused by not considering the emotional and relational framework beyond the cognitive one. Considering the SLDs as a category including various ways of processing information, the purpose for those who work in the education field is to find new strategies of teaching in the classroom. Currently, there are several technology tools with a compensating purpose which have a different effect on the main ways an SLD child organizes, processes, accesses and uses information. Among these, Coding has become world-class for the ability to develop a paradigm shift in the way an SLD person thinks, which is the main issue of the disorder. Coding is a powerful instrument to include and integrate the different learning styles and the cognitive peculiarities of every student: coding 1) reinforces some critical skills (problem solving, the sense of orientation, logical-computational thinking and ability to synthesize information); 2) it generates positive emotionality in the child by involving him in a fun and motivating activity; 3) it offers strategies for dealing with dysgraphia. However, Coding is an instrument and it should be integrated as a part of a training path in order to be effective. The learning courses have to be both designed according to a rigorous method and tailored-made for the specific context (classroom, children, training need analysis). The paper describes in detail the design of a standard-lesson to teach Coding in schools. First of all, we put together the existing platform of coding with compensating software Super Quaderno, a special text editor which can overcome the problems of coordination and short-term memory typical of dysgraphia thanks to its features: phonetic spelling, multimedia objects, automatic association of images, reading word-by-word. The lesson is structured into a) a series of stimulating questions to introduce the topic, dealing with the main resistances and correcting wrong believes; b) a role play that introduce the learners to machine language, stimulating their ability to communicate in a simple and clear way, which is essential for programming; c) a creative exercise that consists in creating an ex novo story using visual blocks of programming: in the first phase the pupil writes a short story, which will be associated with images and sounds; in the second phase, the images are arranged in order to create a labyrinth, while the text disappears; in the last phase, the pupil has to follow the path, made of images and synthesized sounds, in order to recreate the original story. The purpose is to a) stimulate the mind to identify the logical and chronological sense of a text, b) train writing in a ludic manner and c) enhance the typical learning style of children with SLDs, giving way to their talents and creativity. This collaboration can also be extended to other software, to create an interconnected network between programming and compensation tools, potentially useful in different school environments.	[Ronsivalle, G. B.] Univ Verona, Verona, Italy; [Boldi, A.] Wemole Srl, Rome, Italy; [Bazzi, C.] Don G Busato, Vicenza, Italy	University of Verona	Ronsivalle, GB (autor correspondiente), Univ Verona, Verona, Italy.	NaN	Boldi, Arianna/ADK-4069-2022	NaN	NaN	NaN	NaN	NaN	10	1	1	1	12	IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT	VALENICA	LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN	2340-1095	NaN	978-84-697-6957-7	ICERI PROC	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	6930	6938	NaN	NaN	NaN	NaN	NaN	9	Education & Educational Research	Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)	Education & Educational Research	BJ9XB	NaN	NaN	NaN	NaN	2025-12-30	WOS:000429975307009	NaN
5137	5137	J	Ortiz, LG; Bekerman, Z; Ros, MZ	NaN	NaN	NaN	Ortiz, Lourdes Guardia; Bekerman, Zvi; Ros, Miguel Zapata	NaN	NaN	Presentation of the special issue Generative AI, ChatGPT and Education. Consequences for Intelligent Learning and Educational Evaluation	RED-REVISTA DE EDUCACION A DISTANCIA	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	ChatGPT; Generative AI; education; artificial intelligence; instructional design; assessment	COMPUTATIONAL THINKING	In July 2023, given the rise of LLMs (Large Langauge Models), RED convened this special issue on Generative AI and Education, where special attention was paid to its consequences for intelligent learning and educational evaluation. We wanted to give space to contributions that included research related to these topics. Also to experiences about intelligent learning and formative evaluation in ChatGPT contexts. The call was with these general questions center dot Does AI have the potential to revolutionize existing teaching methods, assessment and student support? center dot Creative thinking and problem solving are essential in modern and very complex environments. Could this AI help students deal with these problems? We also had doubts about its benefits. They could be summarized in this question: Generative AI will begin to serve as an active partner in social, creative and intellectual actions continuously over time, and not only as an answer to isolated questions: What are the impacts that will occur? Now those impacts are unknown in the practices that may exist. Another intention was: A theoretical framework is needed to address these questions and in general for an effective deployment of AI systems in education. It is necessary to do so beyond the results provided by empirical research. And that it does not guide and direct at new crossroads, both in research and practice. In the conclusions we see to what extent these expectations have been met. As a consequence, we deduce that the critical importance of theory in the design, development and deployment of AI in education is necessary now more than ever. But we are equally underserved. In this perspective, we continue to critically consider the relevance and continuity of existing learning theories when AI becomes a reality in classrooms. As that result is not met, we also reiterate the call to consider new frameworks, models and ways of thinking. We are referring to those that include the presence of non -human agents, which we hesitate to call a new technology, because it is more like an active partner than a simple technology, as has happened until now. This approach is precisely what makes us insist on a series of important questions for the future, precisely about the review of learning theories based on existing configurations. And to investigate what their alternatives would be in this case. We have done after extensive and exhaustive dissemination in your call. But, despite this and beyond these general conclusions that we have made, the special issue offers us evidence of a scarce empirical investigation of practical cases in the application of generative AI in education. However, of the hundred or so contributions received, seven have been selected in the previous editorial review. The rest have been discarded because they do not conform to the standards or are not the type of contributions requested (the literature reviews per se and the self-report studies stand out among them, due to their high number). Of those seven, six have passed editorial review. They are described at the end. The main contributions of this small number of contributions have been the confirmation of a low level of research and practice. Also, some very interesting contributions from the articles and essays by the invited authors. We draw your attention to these articles and the clear results and evidence obtained on the concrete use of generative AI in specific environments. Results of inevitable use by schools, universities and teachers in these environments or in others to which they can be transferred.	[Ortiz, Lourdes Guardia] Univ Oberta Catalunya, Barcelona, Spain; [Bekerman, Zvi] Hebrew Univ Jerusalem, Jerusalem, Israel; [Ros, Miguel Zapata] Univ Murcia, Murcia, Spain	UOC Universitat Oberta de Catalunya; Hebrew University of Jerusalem; University of Murcia	Ortiz, LG (autor correspondiente), Univ Oberta Catalunya, Barcelona, Spain.	lguardia@uoc.edu; zvi.bekerman@mail.huji.ac.il; mzapata@um.es	BEKERMAN, ZVI/A-1521-2010	BEKERMAN, ZVI/0000-0002-3493-0770	NaN	NaN	NaN	NaN	25	2	6	18	109	UNIV MURCIA	Murcia	Edificio Pleiades Campus de Espinardo, Murcia, 30071, SPAIN	1578-7680	NaN	NaN	RED-REV EDUC DISTANC	RED-Rev. Educ. Distancia	MAY 30	2024	24	78	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10.6018/red.609801	0.0	NaN	NaN	19	Education & Educational Research	Emerging Sources Citation Index (ESCI)	Education & Educational Research	TA3E6	NaN	gold	NaN	NaN	2025-12-30	WOS:001238488500002	10.6018/red.609801
5138	5138	J	Tariq, R; Abatal, M; Vargas, J; Vázquez-Sánchez, AY	NaN	NaN	NaN	Tariq, Rasikh; Abatal, Mohamed; Vargas, Joel; Vazquez-Sanchez, Alma Yolanda	NaN	NaN	Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste	SCIENTIFIC REPORTS	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	3-nitrophenol; Carbonaceous materials; Haematoxylum campechianum; Deep learning; Artificial neural network; Genetic algorithm; Educational innovation; Higher education; Computational thinking; Artificial intelligence	ACTIVATED CARBON; PHENOLIC-COMPOUNDS; SUBSTITUTED PHENOLS; AQUEOUS-SOLUTION; RHIZOPUS-ARRHIZUS; CONGO RED; REMOVAL; WATER; NITROPHENOLS; CHLOROPHENOLS	The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC's surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3-8), adsorbent dosages (2-10 g/L), and temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich-Peterson models. PSO kinetics demonstrated a good fit (R-2 > 0.98) for Ci = 50-100 mg/L, indicating a chemical adsorption mechanism. The Langmuir isotherm model exhibited the best fit, confirming chemical adsorption, with a maximum adsorption capacity (Q(m)) of 236.156 mg/g at T = 300.15 K, pH = 6, contact time = 3 h, and 2 g/L adsorbent dosage. Lower temperatures favored exothermic adsorption. Artificial neural networks (ANNs) were employed for deep learning, optimizing the predictive model for removal percentage. Correlation heat maps highlighted positive correlations between time, dosage, and removal percentage, emphasizing the impact of initial concentration on efficiency. ANN modeling, incorporating iterative optimization, yielded highly accurate predictions, aligned closely with experimental results. The study showcases the success of deep learning in optimizing adsorption processes, emphasizing the importance of diverse correlation algorithms for comprehensive insights into competitive adsorption dynamics. The 5-14-14-1 deep learning architecture, fine-tuned over 228 epochs, demonstrated strong performance with mean squared error (MSE) values of 4.07, 18.406, and 6.2122 for training, testing, and total datasets, respectively, and high R-squared values. Graphical analysis showed a solid linear correlation between experimental and simulated removal percentages, emphasizing the need to consider more than just testing data for optimization. Experimental validation confirmed a 98.77% removal efficiency, illustrating the effectiveness of combining deep learning with genetic algorithms, and highlighting the necessity of experimental trials to verify computational predictions. It is concluded that the carbonaceous material from Haematoxylum campechianum waste (CM-HC) is an effective, low-cost adsorbent for removing 3-nitrophenol from aqueous solutions, achieving optimal removal at pH 6 and 300. 15 K with a maximum adsorption capacity of 236.156 mg/g, following Langmuir model and pseudo-second order kinetics. The validated ANN model offers a reliable tool for practical applications in environmental remediation, advancing both environmental science and educational innovation by integrating artificial neural networks and data science methodologies into student learning experiences.	[Tariq, Rasikh] Tecnol Monterrey, Inst Future Educ, Ave Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico; [Abatal, Mohamed] Univ Autonoma Carmen, Fac Ingn, Ave Cent S-N Esq Con Fracc Mundo Maya, Ciudad Del Carmen 24115, Campeche, Mexico; [Vargas, Joel] Univ Nacl Autonoma Mexico, Unidad Morelia, Inst Invest Mat, Antigua Carretera Patzcuaro 8701,Col Ex Hacienda S, Morelia 58190, Michoacan, Mexico; [Vazquez-Sanchez, Alma Yolanda] Univ Tecnol Xicotepec Juarez, Area Agroind Alimentaria, Ave Univ Tecnol 1000,Col Tierra Negra Xicotepec de, Xicotepec De Juarez 73080, Puebla, Mexico	Tecnologico de Monterrey; Universidad Autonoma del Carmen; Universidad Nacional Autonoma de Mexico	Abatal, M (autor correspondiente), Univ Autonoma Carmen, Fac Ingn, Ave Cent S-N Esq Con Fracc Mundo Maya, Ciudad Del Carmen 24115, Campeche, Mexico.	mabatal@pampano.unacar.mx	Abatal, Mohamed/G-6047-2018; Tariq, Rasikh/AAO-6006-2020; Vargas, Joel/OJV-5248-2025; Alma Yolanda, Vázquez Sánchez/AAP-4797-2021	Alma Yolanda, Vázquez Sánchez/0000-0003-4230-6975	Tecnologico de Monterrey [IJXT070-23EG99001]	Tecnologico de Monterrey(Instituto Politecnico Nacional - Mexico)	The authors would like to thank Tecnologico de Monterrey for the financial support provided through the 'Challenge-Based Research Funding Program 2023', Project ID #IJXT070-23EG99001, entitled 'Complex Thinking Education for All (CTE4A): A Digital Hub and School for Lifelong Learners.'	NaN	107	13	13	9	30	NATURE PORTFOLIO	BERLIN	HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY	2045-2322	NaN	NaN	SCI REP-UK	Sci Rep	AUG 30	2024	14	1	NaN	NaN	NaN	NaN	NaN	NaN	20250	10.1038/s41598-024-70989-0	0.0	NaN	NaN	29	Multidisciplinary Sciences	Science Citation Index Expanded (SCI-EXPANDED)	Science & Technology - Other Topics	E6N2X	39215127.0	Green Submitted, gold	NaN	NaN	2025-12-30	WOS:001304147400093	10.1038/s41598-024-70989-0
5139	5139	C	McMullin, B	NaN	Andrews, P; Caves, L; Doursat, R; Hickinbotham, S; Polack, F; Stepney, S; Taylor, T; Timmis, J	NaN	McMullin, Barry	NaN	NaN	The Overshoot Curriculum: Artificial Life, Education and the Human Predicament	ECAL 2015: THE THIRTEENTH EUROPEAN CONFERENCE ON ARTIFICIAL LIFE	NaN	NaN	English	Proceedings Paper	13th European Conference on Artificial Life (ECAL)	JUL 20-24, 2015	Univ York, York Ctr Complex Syst Anal, York, ENGLAND	MIT Press, Artificial Life,Springer,HIERATIC Project Hierarch Anal Complex Dynam Syst,FoCAS Project Fundamentals Collect Adapt Syst,Earth Life Sci Inst Origins Network,SimOmics	Univ York, York Ctr Complex Syst Anal	NaN	NaN	It is well established in the scientific literature that global human civilization is in serious ecological trouble. The most comprehensive survey is perhaps that of the Planetary Boundaries framework (Rockstrom et al., 2009). The unfolding of these challenges will, of course, be a very complex process; and some detailed impacts are certainly still open to significant human management and moderation. Nonetheless, it seems clear that we are no longer dealing with a problem, or even set of problems, that might be solved; rather, this is a predicament - an uncertain, dynamic, and at least partially chaotic, disruption in global human development (Gilding, 2012). A predicament calls not for solution, but for engagement, and continuous, long term, refinement of response. The purpose of this contribution is to propose a particular educational (curricular and pedagogic) response: one that specifically draws on the tools, techniques and understandings of the field of Artificial Life. In recent decades, the mission of university education, at least in public universities, has become progressively identified simply with the direct support of economic development in its sponsoring regional community. That is, its primary role is to provide graduates with just the knowledge and skills most immediately aligned with the preceived needs of the regional economy. There is a perfectly clear logic and rationale to this development; but in a world faced with global ecological disruption, within the lifetimes of current students, this is neither an honest nor even an effective preparation for the challenges which they will face. Moreover, given that even limited moderation of the impending impacts of ecological limits will rely on the widest societal understanding and deeply informed and engaged leadership at all levels, it is arguable that universities have an immediate, and potentially decisive role to play in communicating the realities of the current global ecological situation. And so, to Artificial Life; or more precisely, to Artificial Ecology. The use of computational tools to model complex biological, evolutionary, ecological and social dynamics is a foundational technique in the ALife field. Indeed, computational thinking and modelling was at the heart of the systems dynamics approach to socio-ecological modelling pioneered by Forrester (1982). This provided the basis for the famous (or infamous?) Limits to Growth (LTG) project of the Club of Rome (Meadows et al., 1972). This was the first substantive attempt to computationally model the socio-ecological dynamics of global human society and assess whether ecological impacts would be likely to limit the growth of human material activities within any practically foreseeable time-frame. While the model was necessarily crude, the robust result was that - in the absence of effective control measures to the contrary - serious limits would become apparent within the first half of the 21st century. In the 40 years since, the world has tracked remarkably close to the standard run of the LTG study (Turner, 2014). In fact, multiple lines of investigation now strongly suggest not just that aggregate human activity is approaching ecological limits, but that it has already reached a state of significant overshoot beyond those limits. Overshoot is a qualitatively distinct regime for the design and operation of any adaptive or mitigating interventions (Catton, 1982). Effective societal responses to date have been significantly impaired by a lack of wide understanding of this harsh ecological reality. This gap in understanding facilitates the comforting - but erroneous - notion that it is prudent to delay difficult responses until after impacts are manifest. But delay is precisely one of the principle mechanisms that actually causes overshoot, and undermines the capability to damp the subsequent crash. Accordingly, it is suggested that there is now a clear need to develop what is here termed an overshoot curriculum, and to integrate this with formation of graduates in all disciplines. Further, a key pedagogical technique in delivering such a curriculum will be the systematic use of computational model ecologies. The Alife community is therefore uniquely positioned to contribute to this radical reform of higher education to meet what are, without exaggeration, the most profound challenges in the history of human civilization.	[McMullin, Barry] Dublin City Univ, Dublin 9, Ireland	Dublin City University	McMullin, B (autor correspondiente), Dublin City Univ, Dublin 9, Ireland.	barry.mcmullin@dcu.ie	McMullin, Barry/B-9504-2009	McMullin, Barry/0000-0002-5789-2068	NaN	NaN	NaN	NaN	6	0	0	0	2	MIT PRESS	CAMBRIDGE	ONE ROGERS ST, CAMBRIDGE, MA 02142 USA	NaN	NaN	NaN	NaN	NaN	NaN	2015	NaN	NaN	NaN	NaN	NaN	NaN	414	414	NaN	10.7551/978-0-262-33027-5-ch073	0.0	NaN	NaN	1	Computer Science, Interdisciplinary Applications; Evolutionary Biology; Mathematical & Computational Biology	Conference Proceedings Citation Index - Science (CPCI-S)	Computer Science; Evolutionary Biology; Mathematical & Computational Biology	BO3ES	NaN	NaN	NaN	NaN	2025-12-30	WOS:000510147800073	10.7551/978-0-262-33027-5-ch073
5140	5140	J	Basaran, M; Vural, ÖF; Metin, S; Tamur, S	NaN	NaN	NaN	Basaran, Mehmet; Vural, omer Faruk; Metin, Sermin; Tamur, Sabiha	NaN	NaN	Early Coding Education and its Multidimensional Impact on Preschool Development: An Analysis of ChatGPT's Insights	INTERNATIONAL JOURNAL OF EARLY CHILDHOOD	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	Coding education; Preschool education; ChatGPT insights; Preschool development	COMPUTATIONAL THINKING; CHILDHOOD; INTERNET; CHILDREN; LEARN	This study investigates ChatGPT's perspectives on coding education for preschool children to provide a comprehensive understanding that is valuable for educators in early childhood education. An instrumental case study approach was employed, utilizing qualitative research design and case study methods. Data were gathered using a structured interview form containing 93 questions posed to ChatGPT version 3.5 to obtain in-depth insights into coding education during the preschool period. Content analysis examined ChatGPT's responses, identifying key themes and codes. The findings indicate that coding practices equip children with coding skills and support their development in multiple domains. Furthermore, the study highlights that uncertainties surrounding the definition of coding, the skills it encompasses, and its integration into curricula are gradually diminishing, with more explicit frameworks emerging. While there are drawbacks to artificial intelligence tools, the study concludes that tools like ChatGPT have significant potential to contribute to the content development of teachers, offering valuable resources to enhance coding education in early childhood. Este estudio investiga las perspectivas de ChatGPT sobre la educaci & oacute;n en programaci & oacute;n para ni & ntilde;os en edad preescolar, con el objetivo de proporcionar una comprensi & oacute;n integral valiosa para los educadores en la educaci & oacute;n infantil. Se emple & oacute; un enfoque de estudio de caso instrumental, utilizando un dise & ntilde;o de investigaci & oacute;n cualitativo y m & eacute;todos de estudio de caso. Los datos se recopilaron mediante un formulario de entrevista estructurada que conten & iacute;a 93 preguntas planteadas a la versi & oacute;n 3.5 de ChatGPT para obtener informaci & oacute;n profunda sobre la educaci & oacute;n en programaci & oacute;n durante el per & iacute;odo preescolar. El an & aacute;lisis de contenido examin & oacute; las respuestas de ChatGPT, identificando temas y c & oacute;digos clave. Los hallazgos indican que las pr & aacute;cticas de programaci & oacute;n equipan a los ni & ntilde;os con habilidades de programaci & oacute;n y apoyan su desarrollo en m & uacute;ltiples & aacute;reas. Adem & aacute;s, el estudio destaca que las incertidumbres en torno a la definici & oacute;n de la programaci & oacute;n, las habilidades que abarca y su integraci & oacute;n en los planes de estudio est & aacute;n disminuyendo gradualmente, con la aparici & oacute;n de marcos m & aacute;s expl & iacute;citos. Si bien existen inconvenientes en las herramientas de inteligencia artificial, el estudio concluye que herramientas como ChatGPT tienen un potencial significativo para contribuir al desarrollo de contenidos para los docentes, ofreciendo recursos valiosos para mejorar la educaci & oacute;n en programaci & oacute;n en la primera infancia. Cette & eacute;tude explore les perspectives de ChatGPT sur l'& eacute;ducation & agrave; la programmation pour les enfants d'& acirc;ge pr & eacute;scolaire, dans le but de fournir une compr & eacute;hension globale pr & eacute;cieuse pour les & eacute;ducateurs en & eacute;ducation de la petite enfance. Une approche d'& eacute;tude de cas instrumental a & eacute;t & eacute; utilis & eacute;e, en adoptant un design de recherche qualitatif et des m & eacute;thodes d'& eacute;tude de cas. Les donn & eacute;es ont & eacute;t & eacute; recueillies & agrave; l'aide d'un formulaire d'entretien structur & eacute; comprenant 93 questions pos & eacute;es & agrave; la version 3.5 de ChatGPT afin d'obtenir des informations approfondies sur l'& eacute;ducation & agrave; la programmation durant la p & eacute;riode pr & eacute;scolaire. Une analyse de contenu a & eacute;t & eacute; r & eacute;alis & eacute;e pour examiner les r & eacute;ponses de ChatGPT, identifiant des th & egrave;mes et codes cl & eacute;s. Les r & eacute;sultats indiquent que les pratiques de programmation dotent les enfants de comp & eacute;tences en codage et soutiennent leur d & eacute;veloppement dans plusieurs domaines. En outre, l'& eacute;tude met en lumi & egrave;re que les incertitudes concernant la d & eacute;finition de la programmation, les comp & eacute;tences qu'elle englobe et son int & eacute;gration dans les programmes scolaires diminuent progressivement, avec l'& eacute;mergence de cadres plus explicites. Bien qu'il existe des limites aux outils d'intelligence artificielle, l'& eacute;tude conclut que des outils comme ChatGPT ont un potentiel significatif pour contribuer au d & eacute;veloppement de contenus pour les enseignants, en offrant des ressources pr & eacute;cieuses pour am & eacute;liorer l'& eacute;ducation & agrave; la programmation en petite enfance.	[Basaran, Mehmet; Tamur, Sabiha] Gaziantep Univ, Fac Educ, Dept Educ Sci, Gaziantep, Turkiye; [Vural, omer Faruk] Sakarya Univ, Fac Educ, Dept Educ Sci, Sakarya, Turkiye; [Metin, Sermin] Hasan Kalyoncu Univ, Fac Educ, Dept Presch Teacher Educ, Gaziantep, Turkiye	Gaziantep University; Sakarya University; Hasan Kalyoncu University	Basaran, M (autor correspondiente), Gaziantep Univ, Fac Educ, Dept Educ Sci, Gaziantep, Turkiye.	mehmetbasaran@outlook.com; omervural@sakarya.edu.tr; sermin.metin@hku.edu.tr; tamur3373@gmail.com	VURAL, OMER FARUK/W-9479-2018; metin, şermin/ABI-5631-2020; Başaran, Mehmet/AAG-5084-2020	VURAL, OMER FARUK/0000-0002-1520-3762; metin, şermin/0000-0001-5984-6359; Tamur, Sabiha/0000-0001-6894-1243; Başaran, Mehmet/0000-0003-1871-520X	NaN	NaN	NaN	NaN	90	1	2	14	31	SPRINGER	DORDRECHT	VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS	0020-7187	1878-4658	NaN	INT J EARLY CHILD	Int. J. Early Child	AUG	2025	57	2	NaN	NaN	NaN	NaN	575	601	NaN	10.1007/s13158-024-00411-3	0.0	NaN	JAN 2025	27	Education & Educational Research	Emerging Sources Citation Index (ESCI)	Education & Educational Research	6VN8V	NaN	NaN	NaN	NaN	2025-12-30	WOS:001398678700001	10.1007/s13158-024-00411-3
5141	5141	J	Morales-Navarro, L; Fields, D; Kafai, YB; Barapatre, D	NaN	NaN	NaN	Morales-Navarro, Luis; Fields, Deborah; Kafai, Yasmin B.; Barapatre, Deepali	NaN	NaN	Assessing changes in thinking about troubleshooting in physical computing: a clinical interview protocol with failure artifacts scenarios	INFORMATION AND LEARNING SCIENCES	NaN	NaN	English	Article	NaN	NaN	NaN	NaN	NaN	Debugging; Troubleshooting; Computer science education; Clinical interview; Assessment; Electronic textiles; Physical computing	COMPUTATIONAL THINKING; DESIGN; EXPLANATIONS; PROGRAMS	PurposeThe purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students' explanations of troubleshooting processes in physical computing activities. The authors focus on physical computing, as finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills.Design/methodology/approachThis paper developed and piloted a failure artifact scenarios clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. Authors applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. They analyzed matching pre- and post-interviews from 18 students at four different schools.FindingsThe findings demonstrate how the protocol can capture change in students' thinking about troubleshooting by eliciting students' explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios and concreteness of troubleshooting and problem-solving processes.Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit. edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1. Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit. edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?	[Morales-Navarro, Luis; Kafai, Yasmin B.; Barapatre, Deepali] Univ Penn, Learning Sci & Technol Program, Philadelphia, PA 19104 USA; [Fields, Deborah] Utah State Univ, Dept Instruct Technol & Learning Sci, Logan, UT USA	University of Pennsylvania; Utah System of Higher Education; Utah State University	Morales-Navarro, L (autor correspondiente), Univ Penn, Learning Sci & Technol Program, Philadelphia, PA 19104 USA.	luismn@upenn.edu; deborah.fields@usu.edu; kafai@upenn.edu; dee2496@upenn.edu	Fields, Deborah/GXZ-6816-2022; Morales-Navarro, Luis/KDN-6301-2024	NaN	National Science Foundation [1742140/#1742081]	National Science Foundation(National Science Foundation (NSF))	With regards to Katherine Gregory for support in data analysis. This work was supported by a grant from the National Science Foundation to Yasmin Kafai and Mike Eisenberg (#1742140/#1742081). Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF, the University of Pennsylvania or Utah State University.	NaN	55	1	1	1	1	EMERALD GROUP PUBLISHING LTD	Leeds	Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE, ENGLAND	2398-5348	2398-5356	NaN	INFORM LEARN SCI	Inf. Learn. Sci.	FEB 24	2025	126	3/4	NaN	NaN	NaN	NaN	286	312	NaN	10.1108/ILS-06-2024-0075	0.0	NaN	JAN 2025	27	Information Science & Library Science	Emerging Sources Citation Index (ESCI)	Information Science & Library Science	X5U6E	NaN	NaN	NaN	NaN	2025-12-30	WOS:001388694200001	10.1108/ils-06-2024-0075

Overview

Variables

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category